WO2013088682A1 - Recommendation condition correction device, recommendation condition correction method, and recommendation condition correction program - Google Patents

Recommendation condition correction device, recommendation condition correction method, and recommendation condition correction program Download PDF

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Publication number
WO2013088682A1
WO2013088682A1 PCT/JP2012/007828 JP2012007828W WO2013088682A1 WO 2013088682 A1 WO2013088682 A1 WO 2013088682A1 JP 2012007828 W JP2012007828 W JP 2012007828W WO 2013088682 A1 WO2013088682 A1 WO 2013088682A1
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WIPO (PCT)
Prior art keywords
recommendation
condition
history
success
user
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PCT/JP2012/007828
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French (fr)
Japanese (ja)
Inventor
健太郎 山崎
敏功 落合
佑嗣 小林
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日本電気株式会社
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Publication of WO2013088682A1 publication Critical patent/WO2013088682A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/105—Human resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06398—Performance of employee with respect to a job function

Definitions

  • Patent Document 1 discloses an apparatus for optimizing an advertisement according to an advertisement result while performing an advertisement. When the sales amount has not reached the target, this device expands the target audience of the advertisement.
  • Patent Document 2 discloses a business analysis table creation system for recognizing the effect of advertising media. This business analysis table displays questionnaire survey responses in a matrix table. This business analysis table includes the number of people who answered that the target product was advertised with a flyer but wanted to purchase the product. Patent Literature 3 discloses a recommendation effect measuring device for an online shopping server.
  • An object of the present invention is to provide a recommended condition correcting device, a recommended condition correcting method, and a recommended condition correcting program that solve the above-mentioned problems.
  • the recommendation condition correction device includes a condition storage unit that stores a recommendation condition that specifies a range of one or more values of the attribute that is user information including a plurality of attributes, When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action.
  • a recommendation history storage means for storing a recommendation history including success / failure information indicating whether or not a predetermined action has been taken;
  • a success history storage means for storing the value of each attribute of the context of the successful user and a recommendation history storage means for each successful user who is a user who has taken the predetermined action other than the target user.
  • the value indicating that the attribute of the context is the value corresponding to each value of the attribute included in the context of all of the recommendation histories being performed indicates that the predetermined action has been taken in the recommendation history
  • a first contribution ratio indicating a ratio of recommendation history is calculated, and a ratio of the success history in which the value of the attribute in the context matches the recommendation condition in the success history for each attribute in the recommendation condition
  • a recommendation condition correction means for correcting the recommendation condition based on the first contribution ratio and the second contribution ratio.
  • a recommendation history including success / failure information indicating whether or not a predetermined action has been taken in the recommendation history storage means;
  • the value of each attribute of the context of the successful user is stored in a success history storage unit, and stored in the recommendation history storage unit.
  • the value indicating that the attribute of the context is the value corresponding to each value of the attribute included in the context of all of the recommendation histories being performed indicates that the predetermined action has been taken in the recommendation history
  • a first contribution ratio indicating a ratio of recommendation history is calculated, and a ratio of the success history in which the value of the attribute in the context matches the recommendation condition in the success history for each attribute in the recommendation condition
  • a second contribution ratio indicating the second contribution ratio is calculated, and the recommendation condition is corrected based on the first contribution ratio and the second contribution ratio.
  • a recommendation condition correction program includes a condition storage process for storing a recommendation condition that specifies a range of one or more values of a context that is user information, including a plurality of attributes, as condition storage means. , When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action.
  • the device can improve the “number of successful recommendations” while suppressing a decrease in the “recommendation success rate”.
  • the recommended condition correction device 10 of the present embodiment includes a registration unit 60, a condition storage unit 40, a recommended condition correction unit 20, a recommendation history storage unit 50, and a success history, each including a logic circuit, a storage device, and the like.
  • a storage unit 30 is included.
  • the recommended condition correction apparatus 10 may be a computer that operates under program control.
  • the registration unit 60, the condition storage unit 40, the recommended condition correction unit 20, the recommendation history storage unit 50, and the success history storage unit 30 are programs stored in the storage device by the processing device included in the computer. It may be realized by reading and executing.
  • the recommendation history storage unit 50, the condition storage unit 40, and the success history storage unit 30 may include a disk device included in the computer.
  • the registration unit 60 receives the scenario 12 from the administrator terminal 11 and registers the condition information 41 in the condition storage unit 40.
  • FIG. 2 shows an example of the scenario 12.
  • the scenario 12 includes a scenario ID, recommendation conditions, and recommendation information.
  • the scenario ID is information for distinguishing the scenario 12.
  • the recommendation condition is information including a recommendation condition ID and a conditional expression.
  • the recommendation condition ID is information for distinguishing the recommendation condition.
  • the conditional expression expresses a condition for selecting a user to be recommended using part or all of the user context 13.
  • the context 13 is information indicating the state of the user, and includes a plurality of attribute information.
  • the attribute information includes a pair of attribute name and attribute value.
  • the attribute name indicates the property of the attribute, and is, for example, a user ID, a position, and an age.
  • the attribute value indicates the value of the attribute, for example, Ichiro Tanaka, XX station, 30 years old.
  • FIG. 3 shows an example of the context 13.
  • This is a context 13 including the attribute information.
  • This context 13 indicates that “User 1 who is a man in his fifties is currently at the station, has 1000 steps, and is in good health”.
  • the conditional expression represents the condition of the context 13 to be satisfied by the user to be recommended by one or more attribute information, that is, one or more attribute name and attribute value pairs.
  • the attribute value may be a range of values.
  • Recommendation information is information related to recommendation.
  • the recommendation information may be a recommendation information ID indicating recommendation content, an address of the recommendation execution device 17 that performs the recommendation, an advertisement that is presented to a user that matches the conditional expression of the recommendation condition, and a program that is executed.
  • FIG. 2 shows a scenario 12 when the recommendation information is a recommendation information ID.
  • the condition information 41 is information including a condition ID, a scenario ID, a conditional expression, and association information.
  • FIG. 4 shows an example of the condition information 41.
  • the registration unit 60 creates the condition information 41 from the scenario 12.
  • the registration unit 60 acquires a recommendation condition ID, a scenario ID, a conditional expression, and recommendation information from the scenario 12, and sets the information as the condition ID, the scenario ID, the conditional expression, and the association information of the condition information 41.
  • the condition storage unit 40 stores one or more condition information 41.
  • FIG. 5 shows an example of a plurality of condition information 41 stored in the condition storage unit 40.
  • FIG. 5 shows an example in which the condition storage unit 40 stores two condition information 41 having a condition ID of recommendation condition 1 and recommendation condition 2.
  • the recommendation history storage unit 50 includes the context 13 at the time of recommendation of the user recommended by the recommendation execution device 17 and whether the user has taken the expected behavior described later (success) or not yet (unsuccessful).
  • One or more recommendation histories 51 including recommendation success / failure information indicating “” are stored.
  • FIG. 6 shows an example of a plurality of recommendation histories 51 stored in the recommendation history storage unit 50.
  • FIG. 6 shows a recommendation history 51 for the recommendation condition 1.
  • the example of FIG. 6 shows that the recommendation execution device 17 made the recommendation four times, and the effect expected only once was obtained.
  • the recommendation execution device 17 receives the user context 13 from the user terminal 15, refers to the condition storage unit 40 included in the recommendation condition correction device 10, transmits recommendation information to the user terminal 15, and recommends The history 51 is written into the recommendation history storage unit 50.
  • the recommendation condition correction apparatus 10 may acquire the recommendation history 51 collectively from the recommendation execution apparatus 17 or the like by other means, for example, a data migration tool or the like.
  • the expected behavior is an effect or reaction expected from the user when the system operator who registered the scenario 12 in the recommendation condition correction system 90 receives a recommendation from the scenario 12. For example, when an advertisement is sent to a user of a specific age group and gender and a purchase of a product is recommended, the expected behavior is that the user purchases the product or the user stops at a store that sells the product. is there.
  • the recommendation execution device 17 may have sent an advertisement for recommending a product to a woman in her 20s, but a woman in her 30s who did not send an advertisement bought the product.
  • the success history 31 is transmitted from the sales terminal of the store to the recommended condition correction device 10, for example.
  • the success history 31 may be collectively transferred from the sales log of the sales terminal of the store by other means such as a data migration tool.
  • the recommendation condition correction apparatus 10 can also acquire the recommendation success / failure information of the recommendation history 51 in the same manner.
  • the recommendation condition correction unit 20 When the recommendation condition correction unit 20 receives the recommendation condition correction request 14 from the administrator terminal 11, the recommendation condition correction unit 20 corrects the conditional expression of the recommendation condition of the recommendation condition ID specified in the recommendation condition correction request 14.
  • the recommendation condition correction unit 20 acquires the success history 31 and the recommendation history 51 from the success history storage unit 30 and the recommendation history storage unit 50, acquires the conditional expression from the condition storage unit 40, and corrects it.
  • the recommendation condition modification request 14 includes a recommendation condition ID, an attribute information deletion threshold value, and an attribute information addition threshold value.
  • FIG. 8 shows an example of the recommended condition correction request 14.
  • the attribute information addition threshold is a lower limit value for adding attribute information when the first success contribution rate of each attribute information calculated from the recommendation history 51 is calculated.
  • the recommendation condition correction unit 20 adds attribute information whose first success contribution rate is above the attribute information addition threshold.
  • the attribute information deletion threshold is a lower limit value that does not delete the attribute information when the second success contribution rate of each attribute information calculated from the success history 31 is calculated.
  • the recommendation condition correction unit 20 deletes attribute information whose second success contribution rate is equal to or less than the attribute information deletion threshold.
  • the recommendation condition modification unit 20 When receiving the recommendation condition modification request 14, the recommendation condition modification unit 20 creates an acquisition request for the condition information 41, an acquisition request for the success history 31, and an acquisition request for the recommendation history 51.
  • the acquisition request for the condition information 41, the acquisition request for the success history 31, and the acquisition request for the recommendation history 51 include a condition ID.
  • the recommended condition correction unit 20 transmits an acquisition request for the condition information 41 to the condition storage unit 40, and acquires the condition information 41 as a response. Also, the recommendation condition correction unit 20 transmits an acquisition request for the success history 31 and an acquisition request for the recommendation history 51 to the success history storage unit 30 and the recommendation history storage unit 50, and the success history 31 and the recommendation history 51 that are responses thereto. Receive.
  • the first success contribution rate is used for adding attribute information.
  • the recommendation condition correction unit 20 extracts all different attribute information, that is, attribute name / attribute value pairs as a list from all the recommendation histories 51 stored in the recommendation history storage unit 50. For each piece of attribute information in the list, the recommendation condition correcting unit 20 out of the recommendation history 51 including the attribute information in the context 13, the number of recommended histories 51 (success number) with the success / failure recommendation is “success”. Is obtained (the number of unsuccessful attempts). Then, the recommendation condition correction unit 20 calculates the number of successes / (number of successes + number of unsuccessful) for each attribute information in the list and sets it as the first success contribution rate.
  • FIG. 9 shows the first success contribution rate calculated from the four recommendation histories 51 shown in FIG.
  • the second success contribution rate is used for deleting attribute information.
  • the recommendation condition correction unit 20 acquires a conditional expression corresponding to the recommendation condition ID included in the recommendation condition correction request 14 from the condition storage unit 40, and includes the attribute information in the context 13 for each attribute information included in the conditional expression.
  • the number of success histories 31 (number of matches) is obtained.
  • the recommendation condition correction unit 20 calculates the number of matches / total number of success histories 31 for each attribute information, and sets it as the second success contribution rate.
  • FIG. 10 shows the second success contribution rate calculated from the ten success histories 31 shown in FIG.
  • the administrator terminal 11 is an information processing apparatus including an input device such as a keyboard and a mouse, an output device such as a liquid crystal display, a processing device that operates under program control, and a storage device including a memory.
  • the administrator terminal 11 creates a scenario 12 and transmits it to the recommended condition correction device 10. Further, the administrator terminal 11 creates a recommended condition correction request 14 and transmits it to the recommended condition correction apparatus 10.
  • the recommendation execution device 17 is an information processing device including an input device such as a keyboard and a mouse, an output device such as a liquid crystal display, a processing device operated by program control, and a storage device including a memory.
  • the recommendation execution device 17 receives the information including the recommendation condition and the recommendation content, and transmits the recommendation information to the user.
  • the operation of the system according to the present embodiment can be divided into a scenario registration flow for registering the scenario 12 and a recommendation condition correction flow for correcting the recommendation conditions.
  • FIG. 11 is a scenario registration flowchart showing the operation of the system according to the first embodiment.
  • the registration unit 60 receives the scenario 12 and proceeds to S-102S.
  • the registration unit 60 acquires a scenario ID, a recommendation condition ID, a conditional expression, and recommendation information from the scenario 12, and sets the condition information 41 using the respective information as a scenario ID, a condition ID, a conditional expression, and association information. Create and go to S-103S.
  • the registration unit 60 transmits the condition information 41 to the condition storage unit 40, and the condition storage unit 40 stores the condition information 41 and ends the scenario registration flow.
  • the linking information is stored in the linking information storage unit.
  • 12A and 12B are recommended condition correction flowcharts showing the operation of the system according to the first embodiment.
  • the recommended condition correction unit 20 receives the recommended condition correction request 14 from the administrator terminal 11, and proceeds to S-1020.
  • the recommendation condition correction request 14 includes a recommendation condition ID, an attribute information deletion threshold value, and an attribute information addition threshold value.
  • the recommended condition modification unit 20 creates a condition acquisition request and proceeds to S-1030.
  • the condition acquisition request includes a recommended condition ID.
  • the recommended condition correction unit 20 transmits a condition acquisition request to the condition storage unit 40.
  • the condition storage unit 40 receives the condition acquisition request, the condition storage unit 40 returns condition information 41 having a condition ID that matches the recommended condition ID included in the condition acquisition request to the recommended condition correction unit 20. If there is no condition information 41 having a matching condition ID, the condition storage unit 40 returns “no match”.
  • the recommended condition correction unit 20 receives the condition information 41 or “no match” from the condition storage unit 40, and proceeds to S-1040.
  • the recommended condition correction unit 20 proceeds to S-1050 if the condition information 41 is received from the condition storage unit 40 in S-1030, and ends the process if “no match” is received.
  • the recommendation condition correction unit 20 creates an acquisition request for the success history 31, and proceeds to S-1060.
  • the acquisition request for the success history 31 includes a recommendation condition ID.
  • the recommendation condition correction unit 20 transmits a success history 31 acquisition request to the success history storage unit 30.
  • the success history storage unit 30 receives the acquisition request for the success history 31, the success history storage unit 30 returns a success history 31 having a condition ID that matches the recommendation condition ID included in the acquisition request for the success history 31 to the recommendation condition correction unit 20. If there is no success history 31 having a matching condition ID, the success history storage unit 30 returns “no match”.
  • the recommendation condition correction unit 20 receives the success history 31 or “no match” from the success history storage unit 30, and proceeds to S-1070.
  • the recommendation condition correction unit 20 creates an acquisition request for the recommendation history 51, and proceeds to S-1080.
  • the acquisition request for the recommendation history 51 includes a recommendation condition ID.
  • the recommendation condition correction unit 20 transmits an acquisition request for the recommendation history 51 to the recommendation history storage unit 50.
  • the recommendation history storage unit 50 Upon receiving the recommendation history 51 acquisition request, the recommendation history storage unit 50 returns a recommendation history 51 having a condition ID that matches the recommendation condition ID included in the recommendation history 51 acquisition request to the recommendation condition correction unit 20. If there is no recommendation history 51 having a matching condition ID, the recommendation history storage unit 50 returns “no match”.
  • the recommendation condition correction unit 20 receives the recommendation history 51 or “no match” from the recommendation history storage unit 50, and proceeds to S-1090.
  • the recommendation condition correction unit 20 proceeds to S-1100 if the success history 31 is received from the success history storage unit 30 in S-1060, and proceeds to S-1110 if “no match” is received.
  • the recommendation condition correction unit 20 performs deletion attribute information determination, and proceeds to S-1110. Detailed operation of attribute information deletion will be described later with reference to FIG.
  • the recommendation condition correction unit 20 proceeds to S-1120 if the recommendation history 51 is received from the recommendation history storage unit 50 in S-1080, and proceeds to S-1130 if “no match” is received.
  • the recommendation condition correction unit 20 performs additional attribute information determination, and proceeds to S-1130.
  • the detailed operation for adding attribute information will be described later with reference to FIG.
  • the recommended condition correction unit 20 deletes the attribute information determined as the deletion attribute information, adds the attribute information determined as the additional attribute information, and ends the process.
  • FIG. 13 is a flowchart of the process for determining the deletion attribute information of the recommendation condition.
  • the recommended condition correction unit 20 extracts a conditional expression from the condition information 41, and proceeds to S-102D.
  • the recommendation condition correction unit 20 sets 0 to the counter i, and proceeds to S-103D.
  • the recommended condition correction unit 20 calculates the second success contribution rate of the i-th attribute information of the conditional expression from the success history 31, and proceeds to S104.
  • the recommendation condition correction unit 20 counts the number of success histories 31 including the i-th attribute information in the context 13 in the success history 31 for all the success histories 31 stored in the success history storage unit 30.
  • the second success contribution rate is obtained by calculating the number of counts / success history 31.
  • the recommended condition modification unit 20 proceeds to S-106D if the calculation of the second success contribution rate has been completed for the attribute information of all the conditional expressions, and proceeds to S-105D if it has not been completed.
  • the recommended condition correction unit 20 adds 1 to the counter i and proceeds to S-103D.
  • the recommendation condition correction unit 20 determines that the attribute information that is below the attribute information deletion threshold among the second success contribution ratio calculated in S-103D is the deletion attribute information, and ends the process.
  • FIG. 14 is a processing flowchart for determining additional attribute information of recommendation conditions.
  • the recommended condition correction unit 20 sets 0 to the counter i, and proceeds to S-102A.
  • the recommendation condition correction unit 20 acquires the i-th recommendation history 51 from the recommendation history 51 acquired from the recommendation history storage unit 50, and proceeds to S-103A.
  • the recommended condition correction unit 20 sets 0 to the counter j, and proceeds to S-104A.
  • the recommendation condition correction unit 20 proceeds to S-105A if the success / failure information of the i-th recommendation history 51 acquired in S-102A is “success”, and proceeds to S-108A if “unsuccessful”. .
  • the recommendation condition correction unit 20 acquires the j-th attribute information of the i-th recommendation history 51, adds 1 to the success number of the attribute information, and proceeds to S-106A.
  • the recommendation condition correcting unit 20 proceeds to S-111A if the processing of S-105A is completed for all the attribute information of the i-th recommendation history 51, and proceeds to S-107A if not completed.
  • the recommended condition correcting unit 20 adds 1 to the counter j and proceeds to S-105A.
  • the recommendation condition correction unit 20 acquires the j-th attribute information of the i-th recommendation history 51, adds 1 to the unsuccessful number of attribute information, and proceeds to S-109A.
  • the recommendation condition correcting unit 20 proceeds to S-111A if the processing of S-108A is completed for all the attribute information of the i-th recommendation history 51, and proceeds to S-110A if not completed.
  • the recommended condition correction unit 20 adds 1 to the counter j and proceeds to S-108A.
  • the recommendation condition correction unit 20 proceeds to A-113A if the processing has been completed for all the recommendation histories 51, and proceeds to S-112A if it has not been completed.
  • the recommended condition correction unit 20 adds 1 to the counter i and proceeds to S-102A.
  • the recommendation condition correction unit 20 calculates the first success contribution rate for each attribute information, and proceeds to S114A.
  • the recommendation condition correcting unit 20 calculates the first success contribution rate by calculating the number of successes / (the number of successes + the number of unsuccessful) for each attribute.
  • the recommendation condition correcting unit 20 determines that the attribute information having the first success contribution rate exceeding the attribute information addition threshold among the attribute information is the additional attribute information, and ends the process.
  • the recommendation condition correction apparatus 10 can perform deletion attribute information determination without using the recommendation history 51 while assuming feedback correction of the recommendation condition based on the recommendation history 51.
  • the recommendation condition correction system 90 increases the number of recommendations to improve the “number of successful recommendations”, while reducing the “recommendation success rate” until the recommendation history 51 is accumulated and fed back. Can be suppressed.
  • the reason is that the recommendation condition correction apparatus 10 performs the deletion attribute information determination using the success history 31 accumulated in the success history storage unit 30.
  • the recommended condition correcting apparatus 10 is based on the assumption that there can be a plurality of recommended conditions.
  • the recommended condition correction apparatus 10 may be implemented on the assumption that there is one recommended condition. In this case, for example, the recommendation history 51 and the success history 31 do not need to include the condition ID, and the recommendation condition correction unit 20 or the like does not need to perform processing for checking the condition ID.
  • the recommendation condition correction apparatus 10 may perform deletion attribute information determination in consideration of the recommendation success rate calculated from the recommendation history 51.
  • the recommendation success rate is a ratio of the number of recommendation histories 51 whose recommendation success / failure information indicates “success” in the recommendation histories 51 stored in the recommendation history storage unit 50.
  • the recommendation condition correction unit 20 may calculate a recommendation success rate, and may perform deletion attribute information determination similar to that of the first embodiment only when this value is equal to or less than a predetermined threshold.
  • This mechanism allows the recommendation condition correcting device 10 to avoid the risk of correcting the recommendation condition and lowering the recommendation success rate when the recommendation success rate is sufficiently high.
  • the recommendation condition correction apparatus 10 may add that the second contribution ratio of the attribute information to be added is a predetermined value or more to the attribute addition condition in the additional attribute information determination. With this mechanism, the recommendation condition correction apparatus 10 can carefully add attribute information.
  • FIG. 15 is a block diagram of the recommended condition correction system 90 according to the second embodiment of this invention.
  • a recommendation condition correction system 90 illustrated in FIG. 15 includes a recommendation condition correction device 10, an administrator terminal 11, a user terminal 15, a recommendation execution device 17, and a success history input terminal 16.
  • the recommended condition correction apparatus 10 of the present embodiment includes a registration unit 60, a condition storage unit 40, a search unit 70, a recommendation history storage unit 50, and a success history registration unit, which are configured with logic circuits, storage devices, and the like. 80, a success history storage unit 30, and a recommendation condition correction unit 20.
  • the recommended condition correction apparatus 10 may be a computer that operates under program control.
  • the registration unit 60, the condition storage unit 40, the search unit 70, the recommendation history storage unit 50, the success history registration unit 80, the success history storage unit 30, and the recommendation condition correction unit 20 are provided in the computer.
  • the central processing unit may be realized by reading and executing a program stored in the storage device.
  • the recommendation history storage unit 50, the condition storage unit 40, and the success history storage unit 30 may include a disk device included in the computer.
  • the registration unit 60, condition storage unit 40, recommendation history storage unit 50, success history storage unit 30, and recommendation condition correction unit 20 in the second embodiment of the present invention are the same as those in the first embodiment.
  • the search unit 70 and the success history registration unit 80 which are differences from the first embodiment, will be described.
  • the search unit 70 receives the user context 13 from the user terminal 15, acquires the condition information 41 that matches the context 13 from the condition storage unit 40, creates a recommendation history 51, and uses the condition information 41 as a recommendation execution device. 17 to send.
  • the search unit 70 creates a recommendation history 51 from the context 13 and the condition information 41 and stores it in the recommendation history storage unit 50.
  • the search unit 70 sets the condition ID obtained from the condition information 41 and the context 13 received from the user terminal 15 to the condition ID and context 13 of the recommendation history 51 to be created.
  • the search unit 70 sets “unsuccessful” information in the recommendation success / failure information of the recommendation history 51. For example, when the context 13 shown in FIG. 3 and the condition information 41 shown in FIG. 4 are obtained, the search unit 70 generates the recommendation history 51 shown in FIG.
  • the search unit 70 transmits the condition information 41 to the recommendation execution device 17.
  • the recommendation execution device 17 executes recommendation for the user terminal 15.
  • the success history registration unit 80 receives the success history 31 from the success history input terminal 16 and determines whether the recommendation history 51 corresponding to the success history 31 exists in the recommendation history storage unit 50.
  • the success history 31 includes a recommendation condition ID and an expected action context 13 and is received when the user takes an action expected by the system operator.
  • FIG. 17 shows an example of the success history 31.
  • the success history 31 shown in FIG. 17 is the success history 31 of the user 1 for the scenario 12 having the recommendation condition indicated by the condition ID of the recommendation condition 1.
  • the success history registration unit 80 When the recommendation history storage unit 50 stores the recommendation history 51 shown in FIG. 6 and the success history registration unit 80 receives the success history 31 shown in FIG. 17, the recommendation history 51 that matches the condition ID and the user ID. Is found. When the recommendation history 51 corresponding to the success history 31 is found, the success history registration unit 80 updates the recommendation success / failure information of the recommendation history 51 of the recommendation history storage unit 50 corresponding to the success history 31 to “success”.
  • the success history registration unit 80 includes the success history 31.
  • the success history registration unit 80 stores the success history 31 in the success history storage unit 30.
  • the user terminal 15 is an information processing apparatus including an input device such as a button, a GPS (Global Positioning System) sensor, an acceleration sensor, and a microphone, an output device such as a liquid crystal display, a processing device, and a storage device such as a memory. is there.
  • an input device such as a button, a GPS (Global Positioning System) sensor, an acceleration sensor, and a microphone
  • an output device such as a liquid crystal display
  • a processing device such as a memory.
  • the user terminal 15 transmits the context 13 to the recommended condition correction device 10 every time there is a change in the value of the input device or at regular intervals.
  • the context 13 is a position acquired by the sensor, a health state input from the input device, or a gender value stored in the storage device.
  • a part of the context 13 such as gender may be stored in the search unit 70 or the like as user profile information in association with the user ID or the like.
  • the search unit 70 adds the stored attribute information to the attribute information received from the user terminal 15 to complete the context 13.
  • the success history input terminal 16 is an information processing device including an input device such as a keyboard and a mouse, an output device such as a liquid crystal display, a processing device operated by program control, and a storage device including a memory. 31 is created and transmitted to the recommended condition correction apparatus 10.
  • the success history input terminal 16 is, for example, a POS terminal (Point of Sales) installed in a store that sells products recommended by the recommendation information.
  • the success history input terminal 16 can receive the context 13 from the user terminal 15 possessed by the user who visited the store, for example, by proximity communication.
  • the success history input terminal 16 stores, for example, correspondence information between sold product codes and recommendation condition IDs, and may create the success history 31 when inputting the sales of products.
  • the operation of the system according to the present embodiment includes a scenario registration flow for registering a scenario 12, a context processing flow performed every time a user context 13 is received, a success history processing flow performed every time a success history 31 is received, and a recommendation It can be divided into a recommended condition correction flow for correcting conditions.
  • the scenario registration flow and recommended condition correction flow are the same as those in the first embodiment.
  • the user context processing flow and the success history processing flow will be described.
  • FIG. 18 is a context processing flowchart showing the operation of the system according to the second embodiment.
  • the search unit 70 receives the context 13 from the user terminal 15, and proceeds to S-202U.
  • the search unit 70 searches the condition storage unit 40 for the condition information 41 that matches the context 13 received in S-201U, and proceeds to S-203U.
  • the condition information 41 that matches the context 13 means the condition information 41 that satisfies the conditional expression that the context 13 includes.
  • the search unit 70 creates a recommendation history 51 from the context 13 and the condition information 41, and proceeds to S-205U.
  • the search unit 70 sets the condition ID obtained from the condition information 41 and the context 13 received from the user terminal 15 to the condition ID and context 13 of the recommendation history 51 to be created.
  • the search unit 70 sets “unsuccessful” information in the recommendation success / failure information of the recommendation history 51.
  • the search unit 70 stores the recommendation history 51 generated in S-204U in the recommendation history storage unit 50, and proceeds to S-206U.
  • the search unit 70 transmits the condition information 41 to the recommendation execution device 17 and ends the process.
  • FIG. 19 is a success history process flowchart showing the operation of the system according to the second embodiment.
  • the success history registration unit 80 receives the success history 31 from the success history input terminal 16, and proceeds to S-202s.
  • the success history registration unit 80 creates a search request for the recommendation history 51 from the success history 31, and proceeds to S-203s.
  • the search request for the recommendation history 51 includes a recommendation condition ID and a user ID included in the success history 31.
  • the success history registration unit 80 searches the recommendation history storage unit 50 for the recommendation history 51 including the same recommendation condition ID and user ID as the recommendation history 51 search request, and proceeds to S-204s.
  • the success history registration unit 80 proceeds to S-205s if there is a matching recommendation history 51 as a result of S-203s, otherwise proceeds to -206s.
  • the success history registration unit 80 updates the recommendation success / failure information of the recommendation history 51 of the recommendation history storage unit 50 including the same recommendation condition ID and user ID as the received success history 31 to “success”, and ends the processing. To do.
  • the success history registration unit 8060 stores the received success history 31 in the success history storage unit 30 and ends the process.
  • the recommendation condition correction apparatus 10 can automatically store the recommendation history 51 and the success history 31 by the apparatus itself. As a result, the degree of freedom in modifying the recommendation conditions is improved, and the burden of creating and maintaining the manager's recommendation history 51 and success history 31 is reduced.
  • the recommendation condition correction apparatus 10 receives the scenario 12 from the administrator terminal 11, the context 13 from the user terminal 15, and the success history 31 from the success history input terminal 16, creates a recommendation history 51, and recommends the recommendation history 51. This is because the success history 31 is accumulated.
  • FIG. 20 is a block diagram of a recommendation condition correction system 90 according to the third embodiment of this invention.
  • a recommendation condition correction system 90 illustrated in FIG. 20 includes a recommendation condition correction apparatus 10, an administrator terminal 11, a user terminal 15, a recommendation condition correction apparatus 10, and a recommendation execution apparatus 17.
  • the recommended condition correction apparatus 10 of the present embodiment includes a registration unit 60, a condition storage unit 40, a search unit 70, a recommendation history storage unit 50, and a success history registration unit, which are configured with logic circuits, storage devices, and the like. 80, a success history storage unit 30, and a recommendation condition correction unit 20.
  • the recommended condition correction apparatus 10 may be a computer that operates under program control.
  • the registration unit 60, the condition storage unit 40, the search unit 70, the recommendation history storage unit 50, the success history registration unit 80, the success history storage unit 30, and the recommendation condition correction unit 20 are provided in the computer.
  • the central processing unit may be realized by reading and executing a program stored in the storage device.
  • the recommendation history storage unit 50, the condition storage unit 40, and the success history storage unit 30 may include a disk device included in the computer.
  • condition storage unit 40, the search unit 70, the recommendation history storage unit 50, the success history registration unit 80, the success history storage unit 30, and the recommendation condition correction unit 20 in the third embodiment of the present invention are the same as those in the second embodiment. It is the same.
  • registration unit 60, the search unit 70, and the success history registration unit 80, which are differences from the second embodiment, will be described.
  • the registration unit 60 receives the scenario 12 and registers the condition information 41 in the condition storage unit 40.
  • FIG. 21 shows an example of the scenario 12.
  • the scenario 12 includes a scenario ID, recommendation conditions, recommendation information, and expected conditions.
  • Scenario ID, recommendation condition, and recommendation information are the same information as in the first embodiment.
  • Expected condition is information including expected condition ID and conditional expression.
  • the expected condition ID is information for distinguishing the expected condition.
  • the conditional expression expresses a state expected of the user as a result of the recommendation using a part or all of the context 13.
  • the recommended condition correcting apparatus 10 determines that the user whose context 13 matches the expected condition has taken the expected behavior.
  • the condition information 41 is information including a scenario ID, a condition ID, a conditional expression, and a condition type.
  • the condition information 41 exists corresponding to the recommended condition.
  • the condition information 41 is created corresponding to both the recommended condition and the expected condition.
  • the condition type indicates whether the condition information 41 corresponds to a recommended condition or an expected condition.
  • the recommendation condition ID of the scenario 12 is stored as the association information.
  • FIG. 22 shows an example of the condition information 41.
  • the condition storage unit 40 stores one or more condition information 41.
  • FIG. 22 shows an example of a plurality of condition information 41 stored in the condition storage unit 40.
  • FIG. 22 shows an example in which the condition storage unit 40 stores four condition information 41 including recommendation condition 1, recommendation condition 2, expectation condition 1, and expectation condition 2.
  • the search unit 70 receives the context 13 from the user terminal 15 and acquires the condition information 41 that matches the context 13 from the condition storage unit 40.
  • the search unit 70 creates the recommendation history 51 and transmits the condition information 41 to the recommendation execution device 17.
  • the search unit 70 transmits the context 13 and the condition information 41 to the success history registration unit 80.
  • the success history registration unit 80 receives the condition information 41 and the context 13 and generates the success history 31 from the condition information 41 and the context 13.
  • the success history registration unit 80 generates the success history 31 from the association information (recommended condition ID) extracted from the condition information 41 and the context 13.
  • the success history registration unit 80 extracts the recommendation condition 1 of the association information (recommended condition ID) from the condition information 41 and matches it with the context 13. 26 creates the success history 31 shown in FIG.
  • the operation of the system according to the present embodiment can be divided into a scenario registration flow for registering a scenario 12, a user context processing flow performed every time a user context 13 is received, and a recommended condition correction flow for correcting a recommendation condition. .
  • the scenario registration flow and recommendation condition correction flow are the same as those in the first implementation system.
  • the user context processing flow will be described.
  • FIG. 27 is a context processing flowchart showing the operation of the system according to the third embodiment.
  • the search unit 70 receives the user context 13 and proceeds to S-302U.
  • the search unit 70 searches the condition storage unit 40 for the condition information 41 that matches the context 13 received in S-301U, and proceeds to S-303U.
  • the search unit 70 proceeds to S-304U if the condition type of the matching condition information 41 is the recommended condition as a result of the search in S-302U, and proceeds to S-307U otherwise.
  • the search unit 70 creates a recommendation history 51 from the context 13 and the condition information 41, and proceeds to S-305U.
  • the search unit 70 sets the condition ID obtained from the condition information 41 and the context 13 received from the user terminal 15 to the condition ID and context 13 of the recommendation history 51 to be created.
  • the search unit 70 sets “unsuccessful” information in the recommendation success / failure information of the recommendation history 51.
  • the search unit 70 stores the recommendation history 51 generated in S-304U in the recommendation history storage unit 50, and proceeds to S-306U.
  • the search unit 70 transmits the condition information 41 to the recommendation execution device 17 and ends the process.
  • the search unit 70 transmits the context 13 and the condition information 41 to the success history registration unit 80, and the S-308U If there is no matching condition information 41, the process is terminated.
  • the success history registration unit 80 In S-308U, the success history registration unit 80 generates a success history 31 from the received context 13 and condition information 41, and proceeds to S-309s.
  • the success history registration unit 80 generates the success history 31 from the association information extracted from the condition information 41 and the context 13.
  • the success history registration unit 80 creates a recommendation history search request from the success history 31, and proceeds to S-310s.
  • the recommendation history search request includes a recommendation condition ID and a user ID included in the success history 31.
  • the success history registration unit 80 searches the recommendation history storage unit 50 for the condition information 41 including the same recommendation condition ID and user ID as the recommendation history search request, and proceeds to S-311U.
  • the success history registration unit 80 proceeds to S-312U if there is matching condition information 41 as a result of S-310U, and proceeds to S-313U if not.
  • the success history registration unit 80 updates the recommendation success / failure information of the recommendation history 51 of the recommendation history storage unit 50 including the same recommendation condition ID and user ID as the success history 31 to “success”, and ends the process.
  • the success history registration unit 80 stores the success history 31 in the success history storage unit 30 and ends the process.
  • the recommendation condition correcting apparatus 10 of the third implementation can automatically store the recommendation history 51 and the success history 31 by the apparatus itself. As a result, the degree of freedom in modifying the recommendation conditions is improved, and the burden of creating / maintaining the recommendation history 51 and the success history 31 of the manager or the like is reduced. Furthermore, it is not necessary to input the success history 31 from the success history input terminal 16, and the burden of creating and maintaining the recommendation history 51 and the success history 31 for the manager and the like is further reduced.
  • the reason is that the recommendation condition correction apparatus 10 receives the scenario 12 from the administrator terminal 11 and the context 13 from the user terminal 15, creates the recommendation history 51, and accumulates the recommendation history 51 and the success history 31. It is.
  • FIG. 28 is a block diagram of the recommended condition correcting apparatus 10 according to the fourth embodiment of this invention.
  • the recommended condition correction apparatus 10 of the present embodiment includes a condition storage unit 40, a recommendation history storage unit 50, a success history storage unit 30, and a recommendation condition correction unit 20.
  • the condition storage unit 40 stores a recommendation condition that specifies a range of values of one or more attributes of the context 13 that is user information including a plurality of attributes.
  • the recommendation history storage unit 50 responds to each target user who is a user who has output recommendation information for encouraging a predetermined action by satisfying the recommendation condition of the context 13, and the value of each attribute of the target user context 13 and A recommendation history 51 including success / failure information indicating whether or not the target user has taken a predetermined action is stored.
  • the success history storage unit 30 stores the value of each attribute of the context 13 of the successful user in response to each successful user who is a user who has taken a predetermined action other than the target user.
  • the recommendation condition modification unit 20 corresponds to each value of each attribute included in the context 13 of all the recommendation histories 51 stored in the recommendation history storage unit 50, and the recommendation history 51 of the recommendation history 51 in which the attribute of the context 13 is the value. Of these, a first contribution ratio indicating the ratio of the recommendation history 51 indicating that a predetermined action has been taken is calculated. Further, the recommendation condition correction unit 20 calculates a second contribution ratio indicating the ratio of the success history 31 in which the value of the attribute of the context 13 matches the recommendation condition in the success history 31 for each attribute in the recommendation condition. Then, the recommendation condition is corrected based on the first contribution rate and the second contribution rate.
  • the recommendation condition correction apparatus 10 performs the deletion attribute information determination using the success history 31 accumulated in the success history storage unit 30.
  • the present invention can be applied to a recommended condition correction system 90 represented by an advertisement distribution system, a control system, a notification system, an expert system, a navigation system, and the like.

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Abstract

Provided is a device capable of effectively correcting a recommendation condition. The device stores recommendation conditions specifying a range of values of attributes of user contexts, and for each user for whom recommendation information has been output, stores a recommendation history including a value of each attribute of the context of the user and success/failure information indicating whether or not the user has taken a predetermined action, and for each user for whom success has been achieved, stores the value of each attribute of the context of the user. In addition, the device calculates, for each value of each attribute included in contexts of all recommendation history entries, a ratio of the recommendation history entries indicating that a predetermined action has been taken among the recommendation history entries for which the attribute of the context is that value, and for each attribute within the recommendation condition, the ratio of successful history entries for which the value of the attribute of the context matches the recommendation condition among the successful history entries. The device corrects the recommendation conditions on the basis of both ratios.

Description

推薦条件修正装置、推薦条件修正方法、および、推薦条件修正プログラムRecommendation condition correcting device, recommended condition correcting method, and recommended condition correcting program
本発明は、推薦条件修正装置、推薦条件修正方法、および、推薦条件修正プログラムに関する。 The present invention relates to a recommended condition correcting device, a recommended condition correcting method, and a recommended condition correcting program.
　人々に、商品購入等の所定の行動を促すための情報である推薦情報、例えば広告、を送付する推薦システムが存在する。推薦システムは、所定の行動を行う可能性が高い人々を選択して、推薦情報を送付しようとする。推薦システムは、推薦対象者を選択するために推薦条件を用いる。 There is a recommendation system for sending recommendation information, for example, advertisements, which is information for encouraging people to perform predetermined actions such as product purchases. The recommendation system selects people who are likely to perform a predetermined action and tries to send recommendation information. The recommendation system uses a recommendation condition to select a recommendation target person.
　推薦システムの推薦性能の指標として、「推薦成功率」と「推薦成功数」の２つがある。推薦成功率は、推薦情報を受けた全ユーザに対して、所定の行動をとったユーザの割合である。推薦成功率は、例えば、ある商品の広告情報を受けた全ユーザに対して、当該商品を購入したユーザの割合である。推薦成功数は、推薦情報を受けた後、所定の行動をとったユーザの数である。 There are two indicators of recommendation performance of the recommendation system: “Recommendation success rate” and “Recommendation success number”. The recommendation success rate is a ratio of users who have taken a predetermined action with respect to all users who have received recommendation information. The recommendation success rate is, for example, a ratio of users who have purchased the product with respect to all users who have received the advertisement information of the product. The number of successful recommendations is the number of users who have taken a predetermined action after receiving recommendation information.
　非特許文献１は、「推薦成功率」と「推薦成功数」の向上を計る推薦システムを開示する。非特許文献１に記載の推薦システムは、「推薦成功数」向上のために、元々の推薦条件を緩和してより多くのユーザに推薦を行う。当該システムは、その後、推薦を実施したときのユーザ情報と所定の行動をとったか否かの成否情報からなる推薦履歴を記録し、推薦履歴を用いて「推薦成功率」向上に向けた推薦条件の修正を実施する。 Non-Patent Document 1 discloses a recommendation system that measures improvement in “recommendation success rate” and “recommendation success number”. The recommendation system described in Non-Patent Document 1 makes recommendations to a larger number of users by relaxing the original recommendation conditions in order to improve the “number of successful recommendations”. The system then records a recommendation history consisting of user information at the time of recommendation and success / failure information indicating whether or not a predetermined action was taken, and using the recommendation history, a recommendation condition for improving the “recommendation success rate” Implement the correction.
　特許文献１は、広告を行いながら、広告結果に応じて広告の最適化を図るための装置を開示する。この装置は、売上金額が目標に達していない場合、広告の対象者を拡大する。 Patent Document 1 discloses an apparatus for optimizing an advertisement according to an advertisement result while performing an advertisement. When the sales amount has not reached the target, this device expands the target audience of the advertisement.
　特許文献２は、広告媒体の効果を認知するための業務分析表作成システムを開示する。この業務分析表は、アンケート調査の回答をマトリックス表により表示する。この業務分析表は、対象商品がチラシで広告されていたことは認知していたが、その商品を購入したかったと回答した人数などを含む。特許文献３は、オンラインショッピングサーバのリコメンド効果測定装置を開示する。 Patent Document 2 discloses a business analysis table creation system for recognizing the effect of advertising media. This business analysis table displays questionnaire survey responses in a matrix table. This business analysis table includes the number of people who answered that the target product was advertised with a flyer but wanted to purchase the product. Patent Literature 3 discloses a recommendation effect measuring device for an online shopping server.
特開２０１０－３９０９５号広報JP 2010-39095 PR 特開２００４－２５８７９６号広報JP 2004-258996 PR 特開２００６－２６０１３５号広報JP 2006-260135 PR
　上記文献が開示する技術はいずれも、推薦条件を効果的に修正する方法を開示しない。非特許文献１の推薦システムは、いったん推薦条件を緩和するため緩和を適切に行わない場合、推薦履歴が十分に集まるまでの期間は、「推薦成功率」が一旦低下してしまう可能性が有る。特許文献１の装置は、広告の対象者を拡大する具体的な方法を開示しない。 None of the techniques disclosed in the above documents disclose a method for effectively correcting recommendation conditions. In the recommendation system of Non-Patent Document 1, if the relaxation is not properly performed once in order to relax the recommendation conditions, there is a possibility that the “recommendation success rate” may once decrease until the recommendation history is sufficiently collected. . The device of Patent Document 1 does not disclose a specific method for expanding the target audience of advertisements.
　本発明の目的は、上記課題を解決する、推薦条件修正装置、推薦条件修正方法、および、推薦条件修正プログラムを提供することである。 An object of the present invention is to provide a recommended condition correcting device, a recommended condition correcting method, and a recommended condition correcting program that solve the above-mentioned problems.
　本発明の一実施形態の推薦条件修正装置は、複数の属性を包含する、ユーザ情報であるコンテキストの１以上の前記属性の値の範囲を指定する推薦条件を格納する条件記憶手段と、
　前記コンテキストが前記推薦条件を満たすことによって、所定の行動を促す推薦情報を出力されたユーザである対象ユーザのおのおの対応に、当該対象ユーザの前記コンテキストの各前記属性の値および当該対象ユーザが前記所定の行動をとったか否かを示す成否情報を含む推薦履歴を格納する推薦履歴記憶手段と、
　前記対象ユーザ以外で前記所定の行動をとったユーザである成功ユーザのおのおの対応に、当該成功ユーザの前記コンテキストの各前記属性の値を格納する成功履歴記憶手段と、前記推薦履歴記憶手段に格納されているすべての前記推薦履歴の前記コンテキストに含まれる各前記属性の各値対応に、前記コンテキストの当該属性が当該値である前記推薦履歴のうち、前記所定の行動をとったことを示す前記推薦履歴の割合を示す第１寄与率を算出し、前記推薦条件内の各前記属性対応に、前記成功履歴のうち、前記コンテキストの当該属性の値が前記推薦条件と一致する前記成功履歴の割合を示す第２寄与率を算出し、前記第１寄与率および前記第２寄与率に基づいて前記推薦条件を修正する推薦条件修正手段を、備える。 The recommendation condition correction device according to an embodiment of the present invention includes a condition storage unit that stores a recommendation condition that specifies a range of one or more values of the attribute that is user information including a plurality of attributes,
When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action. A recommendation history storage means for storing a recommendation history including success / failure information indicating whether or not a predetermined action has been taken;
A success history storage means for storing the value of each attribute of the context of the successful user and a recommendation history storage means for each successful user who is a user who has taken the predetermined action other than the target user. The value indicating that the attribute of the context is the value corresponding to each value of the attribute included in the context of all of the recommendation histories being performed indicates that the predetermined action has been taken in the recommendation history A first contribution ratio indicating a ratio of recommendation history is calculated, and a ratio of the success history in which the value of the attribute in the context matches the recommendation condition in the success history for each attribute in the recommendation condition And a recommendation condition correction means for correcting the recommendation condition based on the first contribution ratio and the second contribution ratio.
　本発明の一実施形態の推薦条件修正方法は、複数の属性を包含する、ユーザ情報であるコンテキストの１以上の前記属性の値の範囲を指定する推薦条件を条件記憶手段に格納し、
　前記コンテキストが前記推薦条件を満たすことによって、所定の行動を促す推薦情報を出力されたユーザである対象ユーザのおのおの対応に、当該対象ユーザの前記コンテキストの各前記属性の値および当該対象ユーザが前記所定の行動をとったか否かを示す成否情報を含む推薦履歴を推薦履歴記憶手段に格納し、
　前記対象ユーザ以外で前記所定の行動をとったユーザである成功ユーザのおのおの対応に、当該成功ユーザの前記コンテキストの各前記属性の値を成功履歴記憶手段に格納し、前記推薦履歴記憶手段に格納されているすべての前記推薦履歴の前記コンテキストに含まれる各前記属性の各値対応に、前記コンテキストの当該属性が当該値である前記推薦履歴のうち、前記所定の行動をとったことを示す前記推薦履歴の割合を示す第１寄与率を算出し、前記推薦条件内の各前記属性対応に、前記成功履歴のうち、前記コンテキストの当該属性の値が前記推薦条件と一致する前記成功履歴の割合を示す第２寄与率を算出し、前記第１寄与率および前記第２寄与率に基づいて前記推薦条件を修正する。 The recommendation condition correction method according to an embodiment of the present invention stores a recommendation condition that specifies a range of one or more values of the attribute that is user information including a plurality of attributes in a condition storage unit,
When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action. Storing a recommendation history including success / failure information indicating whether or not a predetermined action has been taken in the recommendation history storage means;
In response to each successful user who is the user who has taken the predetermined action other than the target user, the value of each attribute of the context of the successful user is stored in a success history storage unit, and stored in the recommendation history storage unit The value indicating that the attribute of the context is the value corresponding to each value of the attribute included in the context of all of the recommendation histories being performed indicates that the predetermined action has been taken in the recommendation history A first contribution ratio indicating a ratio of recommendation history is calculated, and a ratio of the success history in which the value of the attribute in the context matches the recommendation condition in the success history for each attribute in the recommendation condition A second contribution ratio indicating the second contribution ratio is calculated, and the recommendation condition is corrected based on the first contribution ratio and the second contribution ratio.
　本発明の一実施形態の推薦条件修正プログラムは、複数の属性を包含する、ユーザ情報であるコンテキストの１以上の前記属性の値の範囲を指定する推薦条件を条件記憶手段格納する条件記憶処理と、
　前記コンテキストが前記推薦条件を満たすことによって、所定の行動を促す推薦情報を出力されたユーザである対象ユーザのおのおの対応に、当該対象ユーザの前記コンテキストの各前記属性の値および当該対象ユーザが前記所定の行動をとったか否かを示す成否情報を含む推薦履歴を推薦履歴記憶手段に格納する推薦履歴記憶処理と、
　前記対象ユーザ以外で前記所定の行動をとったユーザである成功ユーザのおのおの対応に、当該成功ユーザの前記コンテキストの各前記属性の値を成功履歴記憶手段に格納する処理と、前記推薦履歴記憶手段に格納されているすべての前記推薦履歴の前記コンテキストに含まれる各前記属性の各値対応に、前記コンテキストの当該属性が当該値である前記推薦履歴のうち、前記所定の行動をとったことを示す前記推薦履歴の割合を示す第１寄与率を算出し、前記推薦条件内の各前記属性対応に、前記成功履歴のうち、前記コンテキストの当該属性の値が前記推薦条件と一致する前記成功履歴の割合を示す第２寄与率を算出し、前記第１寄与率および前記第２寄与率に基づいて前記推薦条件を修正する推薦条件修正処理を、コンピュータに実行させる。 A recommendation condition correction program according to an embodiment of the present invention includes a condition storage process for storing a recommendation condition that specifies a range of one or more values of a context that is user information, including a plurality of attributes, as condition storage means. ,
When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action. A recommendation history storage process for storing a recommendation history including success / failure information indicating whether or not a predetermined action has been taken in the recommendation history storage means;
Processing for storing each attribute value of the context of the successful user in a success history storage unit in response to each successful user who is the user who has taken the predetermined action other than the target user, and the recommendation history storage unit In response to each value of each of the attributes included in the context of all the recommendation histories stored in the context, the predetermined action is taken out of the recommendation history in which the attribute of the context is the value Calculating a first contribution ratio indicating a ratio of the recommendation history to be shown, and corresponding to each attribute in the recommendation condition, the success history in which the value of the attribute of the context matches the recommendation condition in the success history And calculating a second contribution ratio indicating a ratio of the first contribution ratio and correcting the recommendation condition based on the first contribution ratio and the second contribution ratio on a computer. To.
　本発明によれば、推薦条件を効果的に修正することができる装置を実現できる。例えば、当該装置は、「推薦成功率」の低下を抑えつつ、「推薦成功数」の向上を図ることが可能となる。 According to the present invention, it is possible to realize an apparatus capable of effectively correcting recommended conditions. For example, the device can improve the “number of successful recommendations” while suppressing a decrease in the “recommendation success rate”.
第１の実施の形態の推薦条件修正システム９０のブロック図である。It is a block diagram of recommendation condition correction system 90 of a 1st embodiment. 第１の実施の形態のシナリオ１２一例を示す図である。It is a figure which shows an example of the scenario 12 of 1st Embodiment. 第１の実施の形態のコンテキスト１３の一例を示す図である。It is a figure which shows an example of the context 13 of 1st Embodiment. 第１の実施の形態の条件情報４１の一例を示す図である。It is a figure which shows an example of the condition information 41 of 1st Embodiment. 第１の実施の形態の条件記憶部４０に記憶される複数の条件情報４１の一例を示す図である。It is a figure which shows an example of the some condition information 41 memorize | stored in the condition memory | storage part 40 of 1st Embodiment. 第１の実施の形態の推薦履歴記憶部５０に記憶される複数の推薦履歴５１の一例を示す図である。It is a figure which shows an example of the some recommendation log | history 51 memorize | stored in the recommendation log | history memory | storage part 50 of 1st Embodiment. 第１の実施の形態の成功履歴記憶部３０に記憶される複数の成功履歴３１の一例を示す図である。It is a figure which shows an example of the several success log | history 31 memorize | stored in the success log | history memory | storage part 30 of 1st Embodiment. 第１の実施の形態の推薦条件修正要求１４の一例を示す図である。It is a figure which shows an example of the recommendation condition correction request | requirement 14 of 1st Embodiment. 第１の実施の形態において、推薦履歴５１から算出した第１成功寄与率の一例を示す図である。It is a figure which shows an example of the 1st success contribution rate computed from the recommendation log | history 51 in 1st Embodiment. 第１の実施の形態において、成功履歴３１から算出した第２成功寄与率の一例を示す図である。In 1st Embodiment, it is a figure which shows an example of the 2nd success contribution rate computed from the success log | history 31. FIG. 第１の実施の形態にかかるシステムの動作を示す、シナリオ登録フローチャートである。It is a scenario registration flowchart which shows operation | movement of the system concerning 1st Embodiment. 第１の実施の形態にかかるシステムの動作を示す、推薦条件修正フローチャート（１／２）である。It is a recommendation condition correction flowchart (1/2) which shows operation | movement of the system concerning 1st Embodiment. 第１の実施の形態にかかるシステムの動作を示す、推薦条件修正フローチャート（２／２）である。It is a recommendation condition correction flowchart (2/2) which shows operation | movement of the system concerning 1st Embodiment. 第１の実施の形態の推薦条件の削除属性情報判定の処理フローチャートである。It is a processing flowchart of deletion attribute information judgment of a recommendation condition of a 1st embodiment. 第１の実施の形態の推薦条件の追加属性情報判定の処理フローチャートである。It is a processing flowchart of additional attribute information determination of recommendation conditions of a 1st embodiment. 第２の実施の形態の推薦条件修正システム９０のブロック図である。It is a block diagram of the recommendation condition correction system 90 of 2nd Embodiment. 第２の実施の形態の推薦履歴５１の一例を示す図である。It is a figure which shows an example of the recommendation log | history 51 of 2nd Embodiment. 第２の実施の形態の成功履歴３１の一例を示す図である。It is a figure which shows an example of the success log | history 31 of 2nd Embodiment. 第２の実施の形態にかかるシステムの動作を示すコンテキスト処理フローチャートである。It is a context process flowchart which shows operation | movement of the system concerning 2nd Embodiment. 第２の実施の形態にかかるシステムの動作を示す成功履歴処理フローチャートである。It is a success history process flowchart which shows operation | movement of the system concerning 2nd Embodiment. 第３の実施の形態の推薦条件修正システム９０のブロック図である。It is a block diagram of the recommendation condition correction system 90 of 3rd Embodiment. 第３の実施の形態のシナリオ１２の一例を示す図である。It is a figure which shows an example of the scenario 12 of 3rd Embodiment. 第３の実施の形態の条件記憶部４０に記憶される複数の条件情報４１の一例を示す図である。It is a figure which shows an example of the several condition information 41 memorize | stored in the condition memory | storage part 40 of 3rd Embodiment. 第３の実施の形態のコンテキスト１３の一例を示す図である。It is a figure which shows an example of the context 13 of 3rd Embodiment. 第３の実施の形態のコンテキスト１３の一例を示す図である。It is a figure which shows an example of the context 13 of 3rd Embodiment. 第３の実施の形態の条件情報４１の一例を示す図である。It is a figure which shows an example of the condition information 41 of 3rd Embodiment. 第３の実施の形態のコンテキスト１３の一例を示す図である。It is a figure which shows an example of the context 13 of 3rd Embodiment. 第３の実施の形態の成功履歴３１の一例を示す図である。It is a figure which shows an example of the success log | history 31 of 3rd Embodiment. 第３実施の形態にかかるシステムの動作を示すコンテキスト処理フローチャートである。It is a context process flowchart which shows operation | movement of the system concerning 3rd Embodiment. 第３の実施の形態の推薦条件修正システム９０のブロック図である。It is a block diagram of the recommendation condition correction system 90 of 3rd Embodiment.
　＜第１の実施の形態＞
　本発明を実施するための形態について図面を参照して詳細に説明する。 <First Embodiment>
Embodiments for carrying out the present invention will be described in detail with reference to the drawings.
　図１は、本発明の第１の実施の形態の推薦条件修正システム９０のブロック図である。図１が示す推薦条件修正システム９０は、推薦条件修正装置１０と、管理者端末１１と、推薦実行装置１７とユーザ端末１５とを含む。 FIG. 1 is a block diagram of a recommended condition correction system 90 according to the first embodiment of this invention. A recommendation condition correction system 90 shown in FIG. 1 includes a recommendation condition correction device 10, an administrator terminal 11, a recommendation execution device 17, and a user terminal 15.
　本実施の形態の推薦条件修正装置１０は、論理回路、記憶装置等で構成される、登録部６０と、条件記憶部４０と、推薦条件修正部２０と、推薦履歴記憶部５０と、成功履歴記憶部３０を含む。推薦条件修正装置１０は、プログラム制御により動作するコンピュータであってもよい。この場合、登録部６０と、条件記憶部４０と、推薦条件修正部２０と、推薦履歴記憶部５０と、成功履歴記憶部３０は、コンピュータが備える処理装置が、記憶装置に格納されたプログラムを読み込んで実行することで実現してもよい。推薦履歴記憶部５０、条件記憶部４０と成功履歴記憶部３０は、コンピュータが備えるディスク装置等を包含してもよい。 The recommended condition correction device 10 of the present embodiment includes a registration unit 60, a condition storage unit 40, a recommended condition correction unit 20, a recommendation history storage unit 50, and a success history, each including a logic circuit, a storage device, and the like. A storage unit 30 is included. The recommended condition correction apparatus 10 may be a computer that operates under program control. In this case, the registration unit 60, the condition storage unit 40, the recommended condition correction unit 20, the recommendation history storage unit 50, and the success history storage unit 30 are programs stored in the storage device by the processing device included in the computer. It may be realized by reading and executing. The recommendation history storage unit 50, the condition storage unit 40, and the success history storage unit 30 may include a disk device included in the computer.
　登録部６０は、管理者端末１１からシナリオ１２を受信し、条件記憶部４０に条件情報４１を登録する。図２はシナリオ１２の一例を示す。シナリオ１２はシナリオIDと、推薦条件と、推薦情報を含む。シナリオIDはシナリオ１２を区別するための情報である。推薦条件は、推薦条件IDと条件式を含む情報である。推薦条件IDは推薦条件を区別するための情報である。条件式は、推薦対象となるユーザを選択するための条件をユーザのコンテキスト１３の一部または全部を用いて表現する。 The registration unit 60 receives the scenario 12 from the administrator terminal 11 and registers the condition information 41 in the condition storage unit 40. FIG. 2 shows an example of the scenario 12. The scenario 12 includes a scenario ID, recommendation conditions, and recommendation information. The scenario ID is information for distinguishing the scenario 12. The recommendation condition is information including a recommendation condition ID and a conditional expression. The recommendation condition ID is information for distinguishing the recommendation condition. The conditional expression expresses a condition for selecting a user to be recommended using part or all of the user context 13.
　ここで、コンテキスト１３は、ユーザの状態を示した情報であり、複数の属性情報を含む。属性情報は属性名と属性値の対を含む。属性名は、属性の性質を示し、例えば、ユーザＩＤ、位置、年齢である。属性値は属性の値を示し、例えば、田中一郎、ＸＸ駅、３０歳である。 Here, the context 13 is information indicating the state of the user, and includes a plurality of attribute information. The attribute information includes a pair of attribute name and attribute value. The attribute name indicates the property of the attribute, and is, for example, a user ID, a position, and an age. The attribute value indicates the value of the attribute, for example, Ichiro Tanaka, XX station, 30 years old.
　図３は、コンテキスト１３の一例を示す。図３が示すコンテキスト１３は、「ユーザID=ユーザ１」、「位置＝駅」、「年齢＝50代」、「性別＝男性」、「歩数＝１０００」、「健康状態＝良好」という6個の属性情報を含むコンテキスト１３である。このコンテキスト１３は、「50代の男性であるユーザ１が現在、駅におり、歩数が1000歩、健康状態は良好である」ことを示している。 FIG. 3 shows an example of the context 13. The contexts 13 shown in FIG. 3 are six such as “user ID = user 1”, “position = station”, “age = 50s”, “sex = male”, “step count = 1000”, and “health state = good”. This is a context 13 including the attribute information. This context 13 indicates that “User 1 who is a man in his fifties is currently at the station, has 1000 steps, and is in good health”.
　条件式は、推薦対象となるユーザが満たすべきコンテキスト１３の条件を、１以上の属性情報、すなわち、１以上の属性名と属性値の対で表す。図２が示す条件式は、「位置＝駅」、「年齢＝50代」、「健康状態＝良好」であるコンテキスト１３を持つユーザが推薦対象であることを示す。なお、属性値は、値の範囲であってもよい。例えば、条件式内の属性情報は、「年齢＝50から54」等でもよい。 The conditional expression represents the condition of the context 13 to be satisfied by the user to be recommended by one or more attribute information, that is, one or more attribute name and attribute value pairs. The conditional expression shown in FIG. 2 indicates that a user having a context 13 with “position = station”, “age = 50s”, and “health condition = good” is a recommendation target. The attribute value may be a range of values. For example, the attribute information in the conditional expression may be “age = 50 to 54” or the like.
　推薦情報は、推薦に関連する情報である。例えば推薦情報は、推薦内容を示す推薦情報IDや、推薦を行う推薦実行装置１７のアドレス、推薦条件の条件式に合致したユーザに提示する広告、実行するプログラムで良い。図２は推薦情報が推薦情報IDである場合のシナリオ１２を示す。 Recommendation information is information related to recommendation. For example, the recommendation information may be a recommendation information ID indicating recommendation content, an address of the recommendation execution device 17 that performs the recommendation, an advertisement that is presented to a user that matches the conditional expression of the recommendation condition, and a program that is executed. FIG. 2 shows a scenario 12 when the recommendation information is a recommendation information ID.
　条件情報４１は、条件ID、シナリオID、条件式、紐付け情報を含む情報である。図４は、条件情報４１の一例を示す。登録部６０は、シナリオ１２から条件情報４１を作成する。登録部６０は、シナリオ１２より推薦条件ID、シナリオID、条件式、推薦情報を取得し、それぞれの情報を条件情報４１の条件ID、シナリオID、条件式、紐付け情報とする。 The condition information 41 is information including a condition ID, a scenario ID, a conditional expression, and association information. FIG. 4 shows an example of the condition information 41. The registration unit 60 creates the condition information 41 from the scenario 12. The registration unit 60 acquires a recommendation condition ID, a scenario ID, a conditional expression, and recommendation information from the scenario 12, and sets the information as the condition ID, the scenario ID, the conditional expression, and the association information of the condition information 41.
　条件記憶部４０は、１以上の条件情報４１を記憶する。図５は、条件記憶部４０に記憶される複数の条件情報４１の一例を示す。図５は、条件記憶部４０が、条件ＩＤが推薦条件１、推薦条件２という2つの条件情報４１を記憶する場合の例を示す。 The condition storage unit 40 stores one or more condition information 41. FIG. 5 shows an example of a plurality of condition information 41 stored in the condition storage unit 40. FIG. 5 shows an example in which the condition storage unit 40 stores two condition information 41 having a condition ID of recommendation condition 1 and recommendation condition 2.
　推薦履歴記憶部５０は、推薦実行装置１７が推薦を行ったユーザの推薦時のコンテキスト１３と、そのユーザが後述する期待行動をとった（成功）のか、未だとっていないか（未成功）のかを示す推薦成否情報を含む推薦履歴５１を１以上記憶する。 The recommendation history storage unit 50 includes the context 13 at the time of recommendation of the user recommended by the recommendation execution device 17 and whether the user has taken the expected behavior described later (success) or not yet (unsuccessful). One or more recommendation histories 51 including recommendation success / failure information indicating “” are stored.
　図６は、推薦履歴記憶部５０に記憶される複数の推薦履歴５１の一例を示す。図６は、推薦条件１に対する推薦履歴５１である。図６の例は、推薦実行装置１７が4回の推薦を行い、そのうち１回のみ期待する効果が得られたことを示す。またこの例は、期待する効果が得られたユーザが推薦を受信した際のコンテキスト１３は、「位置＝駅、年齢＝50代、性別＝女性、歩数＝２０００」であったことを示している。 FIG. 6 shows an example of a plurality of recommendation histories 51 stored in the recommendation history storage unit 50. FIG. 6 shows a recommendation history 51 for the recommendation condition 1. The example of FIG. 6 shows that the recommendation execution device 17 made the recommendation four times, and the effect expected only once was obtained. In addition, this example shows that the context 13 when the user who received the expected effect received the recommendation was “position = station, age = 50s, gender = female, number of steps = 2000”. .
　例えば、推薦実行装置１７は、ユーザ端末１５からユーザのコンテキスト１３を受信して、推薦条件修正装置１０が備える条件記憶部４０を参照して、ユーザ端末１５に対して推薦情報を送信し、推薦履歴５１を推薦履歴記憶部５０に書き込む。推薦条件修正装置１０は、推薦履歴５１を他の手段、例えば、データ移行ツール等によって、推薦実行装置１７等から一括して取得してもよい。 For example, the recommendation execution device 17 receives the user context 13 from the user terminal 15, refers to the condition storage unit 40 included in the recommendation condition correction device 10, transmits recommendation information to the user terminal 15, and recommends The history 51 is written into the recommendation history storage unit 50. The recommendation condition correction apparatus 10 may acquire the recommendation history 51 collectively from the recommendation execution apparatus 17 or the like by other means, for example, a data migration tool or the like.
　成功履歴記憶部３０は、推薦実行装置１７が推薦を行っていないユーザの期待行動時のコンテキスト１３を含む成功履歴３１を複数記憶する。 The success history storage unit 30 stores a plurality of success histories 31 including the context 13 at the time of an expected behavior of a user who is not recommended by the recommendation execution device 17.
　期待行動とは、シナリオ１２を推薦条件修正システム９０に登録したシステム運用者がシナリオ１２による推薦をユーザが受けたときに、ユーザに期待する効果や反応である。例えば、特定の年齢層、性別のユーザに広告を送り、或る商品の購入を勧める場合、期待行動とはユーザがその商品を購入することや、ユーザがその商品を販売する店舗に立寄ることである。 The expected behavior is an effect or reaction expected from the user when the system operator who registered the scenario 12 in the recommendation condition correction system 90 receives a recommendation from the scenario 12. For example, when an advertisement is sent to a user of a specific age group and gender and a purchase of a product is recommended, the expected behavior is that the user purchases the product or the user stops at a store that sells the product. is there.
　推薦サービスにおいては、推薦を行っていないユーザが期待行動を取るということがよく起こる。例えば、推薦実行装置１７が、20代の女性に或る商品の購入をすすめる広告を送っていたが、広告を送っていない30代の女性がその商品を買っていったという場合である。 In the recommendation service, it is common for users who have not made recommendations to take expected actions. For example, the recommendation execution device 17 may have sent an advertisement for recommending a product to a woman in her 20s, but a woman in her 30s who did not send an advertisement bought the product.
　成功履歴記憶部３０では、このような推薦を行っていないにもかかわらず期待行動を取ったユーザの期待行動時のコンテキスト１３を蓄積する。図７は、成功履歴記憶部３０に記憶される複数の成功履歴３１の一例を示す。図７は、推薦条件１に対する成功履歴３１である。図７は、10人のユーザが推薦を受けていないにも拘わらず期待行動を取ったことを示す。 The success history storage unit 30 accumulates the context 13 at the time of the expected behavior of the user who took the expected behavior even though such recommendation is not performed. FIG. 7 shows an example of a plurality of success histories 31 stored in the success history storage unit 30. FIG. 7 shows a success history 31 for the recommendation condition 1. FIG. 7 shows that 10 users have taken expected actions despite not receiving recommendations.
　成功履歴３１は、例えば、店舗の販売端末から推薦条件修正装置１０に送信される。成功履歴３１は、他の手段、例えば、データ移行ツール等によって、店舗の販売端末の販売ログ等から一括して移行されてもよい。推薦条件修正装置１０は、推薦履歴５１の推薦成否情報も同様にして取得できる。 The success history 31 is transmitted from the sales terminal of the store to the recommended condition correction device 10, for example. The success history 31 may be collectively transferred from the sales log of the sales terminal of the store by other means such as a data migration tool. The recommendation condition correction apparatus 10 can also acquire the recommendation success / failure information of the recommendation history 51 in the same manner.
　推薦条件修正部２０は、推薦条件修正要求１４を管理者端末１１から受信すると、推薦条件修正要求１４で指定された推薦条件IDの推薦条件の条件式を修正する。推薦条件修正部２０は、成功履歴記憶部３０および推薦履歴記憶部５０から成功履歴３１と推薦履歴５１を取得し、条件式を条件記憶部４０より取得し、修正する。 When the recommendation condition correction unit 20 receives the recommendation condition correction request 14 from the administrator terminal 11, the recommendation condition correction unit 20 corrects the conditional expression of the recommendation condition of the recommendation condition ID specified in the recommendation condition correction request 14. The recommendation condition correction unit 20 acquires the success history 31 and the recommendation history 51 from the success history storage unit 30 and the recommendation history storage unit 50, acquires the conditional expression from the condition storage unit 40, and corrects it.
　推薦条件修正要求１４は、推薦条件ID、属性情報削除閾値、属性情報追加閾値を含む。図８は推薦条件修正要求１４の一例を示す。属性情報追加閾値は、推薦履歴５１から算出した各属性情報の第１成功寄与率を計算した際に、属性情報を追加する下限の値である。推薦条件修正部２０は、第１成功寄与率が属性情報追加閾値上になる属性情報を追加する。 The recommendation condition modification request 14 includes a recommendation condition ID, an attribute information deletion threshold value, and an attribute information addition threshold value. FIG. 8 shows an example of the recommended condition correction request 14. The attribute information addition threshold is a lower limit value for adding attribute information when the first success contribution rate of each attribute information calculated from the recommendation history 51 is calculated. The recommendation condition correction unit 20 adds attribute information whose first success contribution rate is above the attribute information addition threshold.
　属性情報削除閾値は、成功履歴３１から算出した各属性情報の第２成功寄与率を計算した際に、属性情報を削除しない下限の値である。推薦条件修正部２０は、第２成功寄与率が属性情報削除閾値以下になる属性情報を削除する。 The attribute information deletion threshold is a lower limit value that does not delete the attribute information when the second success contribution rate of each attribute information calculated from the success history 31 is calculated. The recommendation condition correction unit 20 deletes attribute information whose second success contribution rate is equal to or less than the attribute information deletion threshold.
　推薦条件修正部２０は、推薦条件修正要求１４を受信すると、条件情報４１の取得要求、成功履歴３１の取得要求、推薦履歴５１の取得要求を作成する。条件情報４１の取得要求、成功履歴３１の取得要求、推薦履歴５１の取得要求は条件IDを含む。 When receiving the recommendation condition modification request 14, the recommendation condition modification unit 20 creates an acquisition request for the condition information 41, an acquisition request for the success history 31, and an acquisition request for the recommendation history 51. The acquisition request for the condition information 41, the acquisition request for the success history 31, and the acquisition request for the recommendation history 51 include a condition ID.
　推薦条件修正部２０は、条件記憶部４０に対して条件情報４１の取得要求を送信し、その応答である条件情報４１を取得する。また、推薦条件修正部２０は成功履歴記憶部３０および推薦履歴記憶部５０に対して成功履歴３１の取得要求および推薦履歴５１の取得要求を送信し、その応答である成功履歴３１と推薦履歴５１を受信する。 The recommended condition correction unit 20 transmits an acquisition request for the condition information 41 to the condition storage unit 40, and acquires the condition information 41 as a response. Also, the recommendation condition correction unit 20 transmits an acquisition request for the success history 31 and an acquisition request for the recommendation history 51 to the success history storage unit 30 and the recommendation history storage unit 50, and the success history 31 and the recommendation history 51 that are responses thereto. Receive.
　そして、推薦条件修正部２０は、成功履歴３１および推薦履歴５１を用いて属性情報の第１および第２成功寄与率の算出を行い、条件情報４１の条件式に対して、属性情報の追加および削除を行う。 Then, the recommendation condition correction unit 20 calculates the first and second success contribution ratios of the attribute information using the success history 31 and the recommendation history 51, and adds the attribute information to the conditional expression of the condition information 41 and Perform deletion.
　第１成功寄与率の算出の一例について説明する。第１成功寄与率は、属性情報の追加に用いられる。 An example of calculating the first success contribution rate will be described. The first success contribution rate is used for adding attribute information.
　推薦条件修正部２０は、推薦履歴記憶部５０に格納されている全ての推薦履歴５１から、異なった全ての属性情報、すなわち属性名と属性値の対をリストとして抽出する。推薦条件修正部２０は、リスト内の属性情報ごとに、コンテキスト１３に当該属性情報を含む推薦履歴５１のうち、推薦成否が「成功」である推薦履歴５１の数（成功数）および「未成功」である推薦履歴５１の数（未成功数）を求める。そして、推薦条件修正部２０は、リスト内の属性情報ごとに成功数／(成功数＋未成功数)を計算して、第１成功寄与率とする。 The recommendation condition correction unit 20 extracts all different attribute information, that is, attribute name / attribute value pairs as a list from all the recommendation histories 51 stored in the recommendation history storage unit 50. For each piece of attribute information in the list, the recommendation condition correcting unit 20 out of the recommendation history 51 including the attribute information in the context 13, the number of recommended histories 51 (success number) with the success / failure recommendation is “success”. Is obtained (the number of unsuccessful attempts). Then, the recommendation condition correction unit 20 calculates the number of successes / (number of successes + number of unsuccessful) for each attribute information in the list and sets it as the first success contribution rate.
　図９は、図６が示す４つの推薦履歴５１より算出した第１成功寄与率を示す。推薦条件修正部２０は、図８が示す推薦条件修正要求１４を受信した場合、属性情報追加閾値である８０％を上回る属性情報「歩数＝２０００」を推薦条件に追加する。 FIG. 9 shows the first success contribution rate calculated from the four recommendation histories 51 shown in FIG. When the recommendation condition correction unit 20 receives the recommendation condition correction request 14 shown in FIG. 8, the recommendation condition correction unit 20 adds attribute information “step count = 2000” exceeding the attribute information addition threshold of 80% to the recommendation condition.
　　次に、第２成功寄与率の算出の一例について説明する。第２成功寄与率は、属性情報の削除に用いられる。 Next, an example of calculating the second success contribution rate will be described. The second success contribution rate is used for deleting attribute information.
　推薦条件修正部２０は、推薦条件修正要求１４に含まれる推薦条件IDに該当する条件式を条件記憶部４０より取得し、条件式に含まれる属性情報ごとに、コンテキスト１３に当該属性情報を含む成功履歴３１の数（一致数）を求める。推薦条件修正部２０は、属性情報ごとに一致数数／成功履歴３１の総数を計算して、第２成功寄与率とする。 The recommendation condition correction unit 20 acquires a conditional expression corresponding to the recommendation condition ID included in the recommendation condition correction request 14 from the condition storage unit 40, and includes the attribute information in the context 13 for each attribute information included in the conditional expression. The number of success histories 31 (number of matches) is obtained. The recommendation condition correction unit 20 calculates the number of matches / total number of success histories 31 for each attribute information, and sets it as the second success contribution rate.
　図１０は、図７が示す１０個の成功履歴３１より算出した第２成功寄与率を示す。推薦条件修正部２０は、図８が示す推薦条件修正要求１４を受信した場合、属性情報削除閾値である２０％を下回る属性情報「年齢＝５０代」を推薦条件から削除する。 FIG. 10 shows the second success contribution rate calculated from the ten success histories 31 shown in FIG. When the recommendation condition correction unit 20 receives the recommendation condition correction request 14 illustrated in FIG. 8, the recommendation condition correction unit 20 deletes the attribute information “age = 50s” lower than the attribute information deletion threshold 20% from the recommendation condition.
　管理者端末１１は、キーボード、マウスなどの入力装置と、液晶ディスプレイなどの出力装置と、プログラム制御により動作する処理装置と、メモリを含む記憶装置とを備えた情報処理装置である。管理者端末１１は、シナリオ１２を作成し、それを推薦条件修正装置１０へ送信する。また、管理者端末１１は、推薦条件修正要求１４を作成し、推薦条件修正装置１０に送信する。 The administrator terminal 11 is an information processing apparatus including an input device such as a keyboard and a mouse, an output device such as a liquid crystal display, a processing device that operates under program control, and a storage device including a memory. The administrator terminal 11 creates a scenario 12 and transmits it to the recommended condition correction device 10. Further, the administrator terminal 11 creates a recommended condition correction request 14 and transmits it to the recommended condition correction apparatus 10.
　推薦実行装置１７は、キーボード、マウスなどの入力装置と、液晶ディスプレイなどの出力装置と、プログラム制御により動作する処理装置と、メモリを含む記憶装置とを備えた情報処理装置である。推薦実行装置１７は、推薦条件と推薦内容を含む情報を受信し、ユーザに対して推薦情報を送信する。 The recommendation execution device 17 is an information processing device including an input device such as a keyboard and a mouse, an output device such as a liquid crystal display, a processing device operated by program control, and a storage device including a memory. The recommendation execution device 17 receives the information including the recommendation condition and the recommendation content, and transmits the recommendation information to the user.
　次に、フローチャートを参照して本実施の形態の全体の動作について詳細に説明する。本実施の形態のシステムの動作は、シナリオ１２を登録するシナリオ登録フローと、推薦条件の修正を行う推薦条件修正フローに分けることができる。 Next, the overall operation of the present embodiment will be described in detail with reference to a flowchart. The operation of the system according to the present embodiment can be divided into a scenario registration flow for registering the scenario 12 and a recommendation condition correction flow for correcting the recommendation conditions.
　図１１は、第１の実施の形態にかかるシステムの動作を示す、シナリオ登録フローチャートである。 FIG. 11 is a scenario registration flowchart showing the operation of the system according to the first embodiment.
　S-101Sにおいて登録部６０は、シナリオ１２を受信し、S-102Sに進む。S-102Sにおいて登録部６０は、シナリオ１２からシナリオID、推薦条件ID、条件式、推薦情報を取得し、それぞれの情報をシナリオID、条件ID、条件式、紐付け情報とした条件情報４１を作成し、S-103Sに進む。 In S-101S, the registration unit 60 receives the scenario 12 and proceeds to S-102S. In S-102S, the registration unit 60 acquires a scenario ID, a recommendation condition ID, a conditional expression, and recommendation information from the scenario 12, and sets the condition information 41 using the respective information as a scenario ID, a condition ID, a conditional expression, and association information. Create and go to S-103S.
　S-103Sにおいて登録部６０は、条件情報４１を条件記憶部４０に送信し、条件記憶部４０は条件情報４１を記憶し、シナリオ登録フローを終了する。この処理により、紐付け情報が紐付け情報記憶部に記憶される。 In S-103S, the registration unit 60 transmits the condition information 41 to the condition storage unit 40, and the condition storage unit 40 stores the condition information 41 and ends the scenario registration flow. With this process, the linking information is stored in the linking information storage unit.
　図１２Ａ及び図１２Ｂは、第１の実施の形態にかかるシステムの動作を示す、推薦条件修正フローチャートである。 12A and 12B are recommended condition correction flowcharts showing the operation of the system according to the first embodiment.
　S-1010において推薦条件修正部２０は推薦条件修正要求１４を管理者端末１１から受信し、S-1020に進む。推薦条件修正要求１４は、推薦条件ID、属性情報削除閾値、属性情報追加閾値を含む。 In S-1010, the recommended condition correction unit 20 receives the recommended condition correction request 14 from the administrator terminal 11, and proceeds to S-1020. The recommendation condition correction request 14 includes a recommendation condition ID, an attribute information deletion threshold value, and an attribute information addition threshold value.
　S-1020において推薦条件修正部２０は条件取得要求を作成し、S-1030に進む。条件取得要求は推薦条件IDを含む。 In S-1020, the recommended condition modification unit 20 creates a condition acquisition request and proceeds to S-1030. The condition acquisition request includes a recommended condition ID.
　S-1030において推薦条件修正部２０は条件記憶部４０に対して条件取得要求を送信する。条件記憶部４０は条件取得要求を受信すると、条件取得要求に含まれる推薦条件IDに合致する条件IDをもつ条件情報４１を推薦条件修正部２０に返す。合致する条件IDを持つ条件情報４１がない場合、条件記憶部４０は「合致なし」を返す。推薦条件修正部２０は条件記憶部４０より条件情報４１もしくは「合致なし」を受信し、S-1040に進む。 In S-1030, the recommended condition correction unit 20 transmits a condition acquisition request to the condition storage unit 40. When the condition storage unit 40 receives the condition acquisition request, the condition storage unit 40 returns condition information 41 having a condition ID that matches the recommended condition ID included in the condition acquisition request to the recommended condition correction unit 20. If there is no condition information 41 having a matching condition ID, the condition storage unit 40 returns “no match”. The recommended condition correction unit 20 receives the condition information 41 or “no match” from the condition storage unit 40, and proceeds to S-1040.
　S-1040において推薦条件修正部２０は、S-1030にて条件記憶部４０から条件情報４１を受信すればS-1050に進み、「合致なし」を受信すれば処理を終了する。 In S-1040, the recommended condition correction unit 20 proceeds to S-1050 if the condition information 41 is received from the condition storage unit 40 in S-1030, and ends the process if “no match” is received.
　S-1050において推薦条件修正部２０は、成功履歴３１の取得要求を作成し、S-1060に進む。成功履歴３１の取得要求は推薦条件IDを含む。 In S-1050, the recommendation condition correction unit 20 creates an acquisition request for the success history 31, and proceeds to S-1060. The acquisition request for the success history 31 includes a recommendation condition ID.
　S-1060において推薦条件修正部２０は成功履歴記憶部３０に対して成功履歴３１の取得要求を送信する。成功履歴記憶部３０は成功履歴３１の取得要求を受信すると、成功履歴３１の取得要求に含まれる推薦条件IDに合致する条件IDをもつ成功履歴３１を推薦条件修正部２０に返す。合致する条件IDを持つ成功履歴３１がない場合、成功履歴記憶部３０は「合致なし」を返す。推薦条件修正部２０は、成功履歴記憶部３０より成功履歴３１もしくは「合致なし」を受信し、S-1070に進む。 In S-1060, the recommendation condition correction unit 20 transmits a success history 31 acquisition request to the success history storage unit 30. When the success history storage unit 30 receives the acquisition request for the success history 31, the success history storage unit 30 returns a success history 31 having a condition ID that matches the recommendation condition ID included in the acquisition request for the success history 31 to the recommendation condition correction unit 20. If there is no success history 31 having a matching condition ID, the success history storage unit 30 returns “no match”. The recommendation condition correction unit 20 receives the success history 31 or “no match” from the success history storage unit 30, and proceeds to S-1070.
　S-1070において推薦条件修正部２０は、推薦履歴５１の取得要求を作成し、S-1080に進む。推薦履歴５１の取得要求は推薦条件IDを含む。 In S-1070, the recommendation condition correction unit 20 creates an acquisition request for the recommendation history 51, and proceeds to S-1080. The acquisition request for the recommendation history 51 includes a recommendation condition ID.
　S-1080において推薦条件修正部２０は推薦履歴記憶部５０に対して推薦履歴５１の取得要求を送信する。推薦履歴記憶部５０は推薦履歴５１の取得要求を受信すると、推薦履歴５１の取得要求に含まれる推薦条件IDに合致する条件IDをもつ推薦履歴５１を推薦条件修正部２０に返す。合致する条件IDを持つ推薦履歴５１がない場合、推薦履歴記憶部５０は「合致なし」を返す。推薦条件修正部２０は推薦履歴記憶部５０より推薦履歴５１もしくは「合致なし」を受信し、S-1090に進む。 In S-1080, the recommendation condition correction unit 20 transmits an acquisition request for the recommendation history 51 to the recommendation history storage unit 50. Upon receiving the recommendation history 51 acquisition request, the recommendation history storage unit 50 returns a recommendation history 51 having a condition ID that matches the recommendation condition ID included in the recommendation history 51 acquisition request to the recommendation condition correction unit 20. If there is no recommendation history 51 having a matching condition ID, the recommendation history storage unit 50 returns “no match”. The recommendation condition correction unit 20 receives the recommendation history 51 or “no match” from the recommendation history storage unit 50, and proceeds to S-1090.
　S-1090において推薦条件修正部２０は、S-1060にて成功履歴記憶部３０から成功履歴３１を受信すればS-1100に、「合致なし」を受信すればS-1110に進む。 In S-1090, the recommendation condition correction unit 20 proceeds to S-1100 if the success history 31 is received from the success history storage unit 30 in S-1060, and proceeds to S-1110 if “no match” is received.
　S-1100において推薦条件修正部２０は、削除属性情報判定を実施し、S-1110に進む。属性情報削除の詳細動作については図１３を参照して後述する。 In S-1100, the recommendation condition correction unit 20 performs deletion attribute information determination, and proceeds to S-1110. Detailed operation of attribute information deletion will be described later with reference to FIG.
　S-1110において推薦条件修正部２０は、S-1080にて推薦履歴記憶部５０から推薦履歴５１を受信すればS-1120に、「合致なし」を受信すればS-1130に進む。 In S-1110, the recommendation condition correction unit 20 proceeds to S-1120 if the recommendation history 51 is received from the recommendation history storage unit 50 in S-1080, and proceeds to S-1130 if “no match” is received.
　S-1120において推薦条件修正部２０は、追加属性情報判定を実施し、S-1130に進む。属性情報追加の詳細動作については図１４を参照して後述する。 In S-1120, the recommendation condition correction unit 20 performs additional attribute information determination, and proceeds to S-1130. The detailed operation for adding attribute information will be described later with reference to FIG.
　S-1130において推薦条件修正部２０は、削除属性情報判定された属性情報を削除し、追加属性情報判定された属性情報を追加し、処理を終了する。 In S-1130, the recommended condition correction unit 20 deletes the attribute information determined as the deletion attribute information, adds the attribute information determined as the additional attribute information, and ends the process.
　次に、推薦条件修正フローのS-1100およびS-1120にて行われる、削除属性情報判定および追加属性情報判定についてフローチャートを用いて詳細を説明する。 Next, details of the deletion attribute information determination and the additional attribute information determination performed in S-1100 and S-1120 of the recommendation condition correction flow will be described using a flowchart.
　図１３は推薦条件の削除属性情報判定の処理フローチャートである。 FIG. 13 is a flowchart of the process for determining the deletion attribute information of the recommendation condition.
　S-101Dにおいて推薦条件修正部２０は、条件情報４１から条件式を抽出し、S-102Dに進む。S-102Dにおいて推薦条件修正部２０は、カウンタiに0をセットし、S-103Dに進む。 In S-101D, the recommended condition correction unit 20 extracts a conditional expression from the condition information 41, and proceeds to S-102D. In S-102D, the recommendation condition correction unit 20 sets 0 to the counter i, and proceeds to S-103D.
　S-103Dにおいて推薦条件修正部２０は、条件式のi番目の属性情報の第２成功寄与率を成功履歴３１から算出し、S104に進む。推薦条件修正部２０は、成功履歴記憶部３０に格納されているすべての成功履歴３１を対象に、成功履歴３１内のコンテキスト１３にi番目の属性情報を含む成功履歴３１の数をカウントし、カウント数／成功履歴３１の数を計算することで第２成功寄与率を求める。 In S-103D, the recommended condition correction unit 20 calculates the second success contribution rate of the i-th attribute information of the conditional expression from the success history 31, and proceeds to S104. The recommendation condition correction unit 20 counts the number of success histories 31 including the i-th attribute information in the context 13 in the success history 31 for all the success histories 31 stored in the success history storage unit 30. The second success contribution rate is obtained by calculating the number of counts / success history 31.
　S-104Dにおいて推薦条件修正部２０は、全ての条件式の属性情報について第２成功寄与率の算出を終了していればS-106Dへ進み、終了していなければS-105Dに進む。 In S-104D, the recommended condition modification unit 20 proceeds to S-106D if the calculation of the second success contribution rate has been completed for the attribute information of all the conditional expressions, and proceeds to S-105D if it has not been completed.
　S-105Dにおいて推薦条件修正部２０は、カウンタiに1を加算してS-103Dに進む。 In S-105D, the recommended condition correction unit 20 adds 1 to the counter i and proceeds to S-103D.
　S-106Dにおいて推薦条件修正部２０は、S-103Dで算出した第２成功寄与率のうち、属性情報削除閾値を下回る属性情報を削除属性情報と判定し、処理を終了する。 In S-106D, the recommendation condition correction unit 20 determines that the attribute information that is below the attribute information deletion threshold among the second success contribution ratio calculated in S-103D is the deletion attribute information, and ends the process.
　図１４は推薦条件の追加属性情報判定の処理フローチャートである。 FIG. 14 is a processing flowchart for determining additional attribute information of recommendation conditions.
　S-101Aにおいて推薦条件修正部２０は、カウンタiに0をセットし、S-102Aに進む。 In S-101A, the recommended condition correction unit 20 sets 0 to the counter i, and proceeds to S-102A.
　S-102Aにおいて推薦条件修正部２０は、推薦履歴記憶部５０から取得した推薦履歴５１の中からi番目の推薦履歴５１を取得し、S-103Aに進む。 In S-102A, the recommendation condition correction unit 20 acquires the i-th recommendation history 51 from the recommendation history 51 acquired from the recommendation history storage unit 50, and proceeds to S-103A.
　S-103Aにおいて推薦条件修正部２０は、カウンタjに0をセットし、S-104Aに進む。 In S-103A, the recommended condition correction unit 20 sets 0 to the counter j, and proceeds to S-104A.
　S-104Aにおいて推薦条件修正部２０は、S-102Aで取得したi番目の推薦履歴５１の成否情報が「成功」であればS-105Aに、「未成功」であればS-108Aに進む。 In S-104A, the recommendation condition correction unit 20 proceeds to S-105A if the success / failure information of the i-th recommendation history 51 acquired in S-102A is “success”, and proceeds to S-108A if “unsuccessful”. .
　S-105Aにおいて推薦条件修正部２０は、i番目の推薦履歴５１のj番目の属性情報を取得し、属性情報の成功数に1を加算し、S-106Aに進む。 In S-105A, the recommendation condition correction unit 20 acquires the j-th attribute information of the i-th recommendation history 51, adds 1 to the success number of the attribute information, and proceeds to S-106A.
　S-106Aにおいて推薦条件修正部２０は、i番目の推薦履歴５１の全ての属性情報についてS-105Aの処理を終了していればS-111Aに、終了していなければS-107Aに進む。 In S-106A, the recommendation condition correcting unit 20 proceeds to S-111A if the processing of S-105A is completed for all the attribute information of the i-th recommendation history 51, and proceeds to S-107A if not completed.
　S-107Aにおいて推薦条件修正部２０は、カウンタjに1を加算してS-105Aに進む。 In S-107A, the recommended condition correcting unit 20 adds 1 to the counter j and proceeds to S-105A.
　S-108Aにおいて推薦条件修正部２０は、i番目の推薦履歴５１のj番目の属性情報を取得し、属性情報の未成功数に1を加算し、S-109Aに進む。 In S-108A, the recommendation condition correction unit 20 acquires the j-th attribute information of the i-th recommendation history 51, adds 1 to the unsuccessful number of attribute information, and proceeds to S-109A.
　S-109Aにおいて推薦条件修正部２０は、i番目の推薦履歴５１の全ての属性情報についてS-108Aの処理を終了していればS-111Aに、終了していなければS-110Aに進む。 In S-109A, the recommendation condition correcting unit 20 proceeds to S-111A if the processing of S-108A is completed for all the attribute information of the i-th recommendation history 51, and proceeds to S-110A if not completed.
　S-110Aにおいて推薦条件修正部２０は、カウンタjに1を加算してS-108Aに進む。 In S-110A, the recommended condition correction unit 20 adds 1 to the counter j and proceeds to S-108A.
　S-111Aにおいて推薦条件修正部２０は、全ての推薦履歴５１について処理を終了していればA-113Aに、終了していなければS-112Aに進む。 In S-111A, the recommendation condition correction unit 20 proceeds to A-113A if the processing has been completed for all the recommendation histories 51, and proceeds to S-112A if it has not been completed.
　S-112Aにおいて推薦条件修正部２０は、カウンタiに1を加算してS-102Aに進む。 In S-112A, the recommended condition correction unit 20 adds 1 to the counter i and proceeds to S-102A.
　S-113Aにおいて推薦条件修正部２０は、各属性情報について第１成功寄与率を算出し、S114Aに進む。推薦条件修正部２０は、各属性対応に、成功数／（成功数＋未成功数）を計算することで第１成功寄与率を求める。 In S-113A, the recommendation condition correction unit 20 calculates the first success contribution rate for each attribute information, and proceeds to S114A. The recommendation condition correcting unit 20 calculates the first success contribution rate by calculating the number of successes / (the number of successes + the number of unsuccessful) for each attribute.
　S-114Aにおいて推薦条件修正部２０は、各属性情報のうち第１成功寄与率が、属性情報追加閾値を上回る属性情報を追加属性情報と判定し、処理を終了する。 In S-114A, the recommendation condition correcting unit 20 determines that the attribute information having the first success contribution rate exceeding the attribute information addition threshold among the attribute information is the additional attribute information, and ends the process.
　本実施の形態の推薦条件修正装置１０は、推薦履歴５１に基づく推薦条件のフィードバック修正を前提としながらも、推薦履歴５１を利用せずに削除属性情報判定を行えるようになる。その結果、推薦条件修正システム９０は、推薦数の拡大を行って「推薦成功数」の向上を計る一方、推薦履歴５１が集積されてフィードバックがされるまでの間の「推薦成功率」の低下を抑制することが可能となる。その理由は、推薦条件修正装置１０は、成功履歴記憶部３０に蓄積された成功履歴３１を用いて削除属性情報判定を行うからである。 The recommendation condition correction apparatus 10 according to the present embodiment can perform deletion attribute information determination without using the recommendation history 51 while assuming feedback correction of the recommendation condition based on the recommendation history 51. As a result, the recommendation condition correction system 90 increases the number of recommendations to improve the “number of successful recommendations”, while reducing the “recommendation success rate” until the recommendation history 51 is accumulated and fed back. Can be suppressed. The reason is that the recommendation condition correction apparatus 10 performs the deletion attribute information determination using the success history 31 accumulated in the success history storage unit 30.
　＜第１の実施形態の変形＞
　第１の実施形態の推薦条件修正装置１０は、推薦条件が複数あり得ることを前提としていた。推薦条件修正装置１０は、推薦条件が一つであることを前提として実装されてもよい。この場合、例えば、推薦履歴５１や成功履歴３１は、条件IDを包含する必要はなく、推薦条件修正部２０等は、条件IDを照合する処理などは不要となる。 <Modification of First Embodiment>
The recommended condition correcting apparatus 10 according to the first embodiment is based on the assumption that there can be a plurality of recommended conditions. The recommended condition correction apparatus 10 may be implemented on the assumption that there is one recommended condition. In this case, for example, the recommendation history 51 and the success history 31 do not need to include the condition ID, and the recommendation condition correction unit 20 or the like does not need to perform processing for checking the condition ID.
　また、推薦条件修正装置１０は、推薦履歴５１から算出される推薦成功率を考慮して、削除属性情報判定を行ってもよい。ここで、推薦成功率は、推薦履歴記憶部５０に格納されている推薦履歴５１のうち、推薦成否の情報が「成功」を示す推薦履歴５１の数の割合である。具体的には、推薦条件修正部２０は、推薦成功率を算出し、この値が所定閾値以下である場合に限り、第１の実施形態と同様の削除属性情報判定を行ってもよい。 Also, the recommendation condition correction apparatus 10 may perform deletion attribute information determination in consideration of the recommendation success rate calculated from the recommendation history 51. Here, the recommendation success rate is a ratio of the number of recommendation histories 51 whose recommendation success / failure information indicates “success” in the recommendation histories 51 stored in the recommendation history storage unit 50. Specifically, the recommendation condition correction unit 20 may calculate a recommendation success rate, and may perform deletion attribute information determination similar to that of the first embodiment only when this value is equal to or less than a predetermined threshold.
　この仕組みにより、推薦条件修正装置１０は、推薦成功率が十分高い場合に、推薦条件を修正して推薦成功率を下げてしまうリスクを回避できる。 This mechanism allows the recommendation condition correcting device 10 to avoid the risk of correcting the recommendation condition and lowering the recommendation success rate when the recommendation success rate is sufficiently high.
　さらに、推薦条件修正装置１０は、追加属性情報判定において、属性追加の条件に、追加対象となる属性情報の第２寄与率が所定値以上であることを追加してもよい。この仕組みにより、推薦条件修正装置１０は、属性情報の追加を慎重に行うことができる。 Furthermore, the recommendation condition correction apparatus 10 may add that the second contribution ratio of the attribute information to be added is a predetermined value or more to the attribute addition condition in the additional attribute information determination. With this mechanism, the recommendation condition correction apparatus 10 can carefully add attribute information.
　＜第２の実施の形態＞
　次に、本発明の第２の実施の形態について図面を参照して詳細に説明する。 <Second Embodiment>
Next, a second embodiment of the present invention will be described in detail with reference to the drawings.
　図１５は、本発明の第２の実施の形態の推薦条件修正システム９０のブロック図である。図１５が示す推薦条件修正システム９０は、推薦条件修正装置１０と、管理者端末１１と、ユーザ端末１５と、推薦実行装置１７と、成功履歴入力端末１６とを含む。 FIG. 15 is a block diagram of the recommended condition correction system 90 according to the second embodiment of this invention. A recommendation condition correction system 90 illustrated in FIG. 15 includes a recommendation condition correction device 10, an administrator terminal 11, a user terminal 15, a recommendation execution device 17, and a success history input terminal 16.
　本実施の形態の推薦条件修正装置１０は、論理回路、記憶装置等で構成される、登録部６０と、条件記憶部４０と、検索部７０と、推薦履歴記憶部５０と、成功履歴登録部８０と、成功履歴記憶部３０と、推薦条件修正部２０とを含む。推薦条件修正装置１０は、プログラム制御により動作するコンピュータであってもよい。この場合、登録部６０と、条件記憶部４０と、検索部７０と、推薦履歴記憶部５０と、成功履歴登録部８０と、成功履歴記憶部３０と、推薦条件修正部２０は、コンピュータが備える中央処理装置が、記憶装置に格納されたプログラムを読み込んで実行することで実現されてもよい。推薦履歴記憶部５０、条件記憶部４０と成功履歴記憶部３０は、コンピュータが備えるディスク装置等を包含してもよい。 The recommended condition correction apparatus 10 of the present embodiment includes a registration unit 60, a condition storage unit 40, a search unit 70, a recommendation history storage unit 50, and a success history registration unit, which are configured with logic circuits, storage devices, and the like. 80, a success history storage unit 30, and a recommendation condition correction unit 20. The recommended condition correction apparatus 10 may be a computer that operates under program control. In this case, the registration unit 60, the condition storage unit 40, the search unit 70, the recommendation history storage unit 50, the success history registration unit 80, the success history storage unit 30, and the recommendation condition correction unit 20 are provided in the computer. The central processing unit may be realized by reading and executing a program stored in the storage device. The recommendation history storage unit 50, the condition storage unit 40, and the success history storage unit 30 may include a disk device included in the computer.
　本発明の第２の実施の形態における、登録部６０、条件記憶部４０、推薦履歴記憶部５０、成功履歴記憶部３０、推薦条件修正部２０は第１の実施の形態と同様である。以下、第１の実施の形態との差分となる、検索部７０と成功履歴登録部８０について説明する。 The registration unit 60, condition storage unit 40, recommendation history storage unit 50, success history storage unit 30, and recommendation condition correction unit 20 in the second embodiment of the present invention are the same as those in the first embodiment. Hereinafter, the search unit 70 and the success history registration unit 80, which are differences from the first embodiment, will be described.
　検索部７０は、ユーザ端末１５からユーザのコンテキスト１３を受信し、条件記憶部４０から当該コンテキスト１３に合致する条件情報４１を取得し、推薦履歴５１を作成するとともに、条件情報４１を推薦実行装置１７に送信する。 The search unit 70 receives the user context 13 from the user terminal 15, acquires the condition information 41 that matches the context 13 from the condition storage unit 40, creates a recommendation history 51, and uses the condition information 41 as a recommendation execution device. 17 to send.
　例えば、図５が示す複数の条件情報４１が条件記憶部４０に記憶されている場合、検索部７０は、図３が示すコンテキスト１３を受信すると、コンテキスト１３が「位置＝駅、年齢＝50代、性別＝男性、歩数＝１０００、健康状態＝良好」に合致する条件情報４１を取得する。 For example, when a plurality of condition information 41 shown in FIG. 5 is stored in the condition storage unit 40, when the search unit 70 receives the context 13 shown in FIG. 3, the context 13 is “location = station, age = 50s”. , Condition information 41 matching “sex = male, number of steps = 1000, health condition = good” is acquired.
　取得される条件情報４１は、条件IDが推薦条件1、シナリオIDがシナリオ1、条件式が「位置=駅 & 年齢=50代 & 健康状態=良好」紐付け情報が推薦情報1の条件情報４１である。 The acquired condition information 41 is condition information 41 where the condition ID is recommendation condition 1, the scenario ID is scenario 1, and the conditional expression is “position = station & age = 50's & health state = good”. It is.
　検索部７０は、コンテキスト１３および条件情報４１から推薦履歴５１を作成し、推薦履歴記憶部５０に記憶させる。ここで、検索部７０は、作成する推薦履歴５１の条件IDおよびコンテキスト１３に、条件情報４１から得た条件IDおよびユーザ端末１５から受信したコンテキスト１３をおのおの設定する。検索部７０は、推薦履歴５１の推薦成否情報に、「未成功」の情報を設定する。例えば、図３が示すコンテキスト１３と図４が示す条件情報４１を得た場合、検索部７０は図１６が示す推薦履歴５１を生成する。 The search unit 70 creates a recommendation history 51 from the context 13 and the condition information 41 and stores it in the recommendation history storage unit 50. Here, the search unit 70 sets the condition ID obtained from the condition information 41 and the context 13 received from the user terminal 15 to the condition ID and context 13 of the recommendation history 51 to be created. The search unit 70 sets “unsuccessful” information in the recommendation success / failure information of the recommendation history 51. For example, when the context 13 shown in FIG. 3 and the condition information 41 shown in FIG. 4 are obtained, the search unit 70 generates the recommendation history 51 shown in FIG.
　さらに、検索部７０は、条件情報４１を推薦実行装置１７に送信する。推薦実行装置１７は、ユーザ端末１５に対して推薦を実行する。 Further, the search unit 70 transmits the condition information 41 to the recommendation execution device 17. The recommendation execution device 17 executes recommendation for the user terminal 15.
　成功履歴登録部８０は、成功履歴入力端末１６から成功履歴３１を受信し、成功履歴３１に対応する推薦履歴５１が推薦履歴記憶部５０に存在するかを判定する。 The success history registration unit 80 receives the success history 31 from the success history input terminal 16 and determines whether the recommendation history 51 corresponding to the success history 31 exists in the recommendation history storage unit 50.
　成功履歴３１は推薦条件ID、期待行動時のコンテキスト１３からなり、ユーザが、システム運用者が期待する行動を取った場合に受信する。 The success history 31 includes a recommendation condition ID and an expected action context 13 and is received when the user takes an action expected by the system operator.
　図１７は、成功履歴３１の一例を示す。図１７が示す成功履歴３１は、推薦条件１の条件IDで示される推薦条件をもつシナリオ１２に対するユーザ1の成功履歴３１である。この成功履歴３１は、期待行動時のコンテキスト１３が「位置＝公園、年齢＝50代、性別＝男、歩数＝３０００、健康状態＝良好」であることを示している。 FIG. 17 shows an example of the success history 31. The success history 31 shown in FIG. 17 is the success history 31 of the user 1 for the scenario 12 having the recommendation condition indicated by the condition ID of the recommendation condition 1. The success history 31 indicates that the context 13 at the time of expected behavior is “position = park, age = 50s, gender = male, number of steps = 3000, health condition = good”.
　推薦履歴記憶部５０が、図６が示す推薦履歴５１を記憶しており、成功履歴登録部８０が、図１７が示す成功履歴３１を受信した場合、条件IDおよびユーザIDの合致する推薦履歴５１が見つかる。成功履歴３１に対応する推薦履歴５１が見つかった場合、成功履歴登録部８０は、成功履歴３１に対応する推薦履歴記憶部５０の推薦履歴５１の推薦成否情報を「成功」に更新する。 When the recommendation history storage unit 50 stores the recommendation history 51 shown in FIG. 6 and the success history registration unit 80 receives the success history 31 shown in FIG. 17, the recommendation history 51 that matches the condition ID and the user ID. Is found. When the recommendation history 51 corresponding to the success history 31 is found, the success history registration unit 80 updates the recommendation success / failure information of the recommendation history 51 of the recommendation history storage unit 50 corresponding to the success history 31 to “success”.
　例えば、図６が示す推薦履歴５１を推薦履歴記憶部５０が記憶しており、成功履歴登録部８０が、図１７が示す成功履歴３１を受信した場合、成功履歴登録部８０は、成功履歴３１の「条件ID＝推薦条件1、ユーザID＝ユーザ1」に合致する推薦履歴５１の推薦成否情報を未成功から成功に更新する。この推薦履歴５１は、「条件IDが推薦条件1、推薦時コンテキスト１３が、ユーザID＝ユーザ1、位置＝駅、年齢＝50代、性別＝男性、歩数＝１０００、健康状態＝良好」の推薦履歴５１である。 For example, when the recommendation history storage unit 50 stores the recommendation history 51 illustrated in FIG. 6 and the success history registration unit 80 receives the success history 31 illustrated in FIG. 17, the success history registration unit 80 includes the success history 31. The recommendation success / failure information of the recommendation history 51 that matches “condition ID = recommendation condition 1, user ID = user 1” is updated from unsuccessful to successful. This recommendation history 51 includes a recommendation that “condition ID is recommendation condition 1, recommendation context 13 is user ID = user 1, position = station, age = 50s, gender = male, number of steps = 1000, health condition = good”. This is a history 51.
　成功履歴３１に対応する推薦履歴５１が見つからなかった場合、成功履歴登録部８０は、成功履歴３１を成功履歴記憶部３０に記憶させる。 When the recommendation history 51 corresponding to the success history 31 is not found, the success history registration unit 80 stores the success history 31 in the success history storage unit 30.
　ユーザ端末１５は、ボタン、ＧＰＳ（Global Positioning System）センサ、加速度センサ、マイクなどの入力装置と、液晶ディスプレイなどの出力装置と、処理装置と、メモリなどの記憶装置とを備えた情報処理装置である。 The user terminal 15 is an information processing apparatus including an input device such as a button, a GPS (Global Positioning System) sensor, an acceleration sensor, and a microphone, an output device such as a liquid crystal display, a processing device, and a storage device such as a memory. is there.
　このユーザ端末１５は、入力装置の値に変化がある度もしくは一定間隔で推薦条件修正装置１０に対して、コンテキスト１３を送信する。コンテキスト１３は、センサで取得された位置、入力装置から入力された健康状態、または、記憶装置に記憶されていた性別等の値である。なお、性別など一部のコンテキスト１３は、ユーザプロファイル情報として、ユーザＩＤ等に対応付けて、検索部７０等が記憶していてもよい。この場合、例えば、検索部７０は、ユーザ端末１５から受信した属性情報に、記憶している属性情報を追加して、コンテキスト１３を完成させる。 The user terminal 15 transmits the context 13 to the recommended condition correction device 10 every time there is a change in the value of the input device or at regular intervals. The context 13 is a position acquired by the sensor, a health state input from the input device, or a gender value stored in the storage device. Note that a part of the context 13 such as gender may be stored in the search unit 70 or the like as user profile information in association with the user ID or the like. In this case, for example, the search unit 70 adds the stored attribute information to the attribute information received from the user terminal 15 to complete the context 13.
　成功履歴入力端末１６は、キーボード、マウスなどの入力装置と、液晶ディスプレイなどの出力装置と、プログラム制御により動作する処理装置と、メモリを含む記憶装置とを備えた情報処理装置であり、成功履歴３１を作成し、それを推薦条件修正装置１０へ送信する。 The success history input terminal 16 is an information processing device including an input device such as a keyboard and a mouse, an output device such as a liquid crystal display, a processing device operated by program control, and a storage device including a memory. 31 is created and transmitted to the recommended condition correction apparatus 10.
　成功履歴入力端末１６は、例えば、推薦情報で推薦された商品を販売する店舗に設置されているＰＯＳ端末（Point of Sales）である。成功履歴入力端末１６は、例えば、近接通信により、店舗を訪れたユーザが所持するユーザ端末１５からコンテキスト１３を受信可能である。また、成功履歴入力端末１６は、例えば、売れた商品コードと推薦条件IDとの対応情報を記憶しており、商品の売り上げ入力時に、成功履歴３１を作成してもよい。 The success history input terminal 16 is, for example, a POS terminal (Point of Sales) installed in a store that sells products recommended by the recommendation information. The success history input terminal 16 can receive the context 13 from the user terminal 15 possessed by the user who visited the store, for example, by proximity communication. In addition, the success history input terminal 16 stores, for example, correspondence information between sold product codes and recommendation condition IDs, and may create the success history 31 when inputting the sales of products.
　次に、フローチャートを参照して本実施の形態の全体の動作について詳細に説明する。本実施の形態のシステムの動作は、シナリオ１２を登録するシナリオ登録フローと、ユーザのコンテキスト１３を受信する度に行うコンテキスト処理フロー、成功履歴３１を受信する度に行う成功履歴処理フローと、推薦条件の修正を行う推薦条件修正フローに分けることができる。 Next, the overall operation of the present embodiment will be described in detail with reference to a flowchart. The operation of the system according to the present embodiment includes a scenario registration flow for registering a scenario 12, a context processing flow performed every time a user context 13 is received, a success history processing flow performed every time a success history 31 is received, and a recommendation It can be divided into a recommended condition correction flow for correcting conditions.
　シナリオ登録フローと推薦条件修正フローは、第１の実施の形態と同様である。以下、ユーザコンテキスト処理フローと成功履歴処理フローについて説明する。 The scenario registration flow and recommended condition correction flow are the same as those in the first embodiment. Hereinafter, the user context processing flow and the success history processing flow will be described.
　図１８は第２の実施の形態にかかるシステムの動作を示すコンテキスト処理フローチャートである。 FIG. 18 is a context processing flowchart showing the operation of the system according to the second embodiment.
　S-201Uにおいて検索部７０は、コンテキスト１３をユーザ端末１５から受信し、S-202Uに進む。 In S-201U, the search unit 70 receives the context 13 from the user terminal 15, and proceeds to S-202U.
　S-202Uにおいて検索部７０は、S-201Uで受信したコンテキスト１３に合致する条件情報４１を条件記憶部４０から検索し、S-203Uに進む。コンテキスト１３に合致する条件情報４１とは、当該コンテキスト１３が、条件情報４１が包含する条件式を満足する条件情報４１を意味する。 In S-202U, the search unit 70 searches the condition storage unit 40 for the condition information 41 that matches the context 13 received in S-201U, and proceeds to S-203U. The condition information 41 that matches the context 13 means the condition information 41 that satisfies the conditional expression that the context 13 includes.
　S-203Uにおいて検索部７０は、S-202Uの検索の結果、合致する条件情報４１があれば、S-204Uに進み、なければ処理を終了する。 In S-203U, if there is matching condition information 41 as a result of the search in S-202U, the search unit 70 proceeds to S-204U, and if not, ends the processing.
　S-204Uにおいて検索部７０は、コンテキスト１３および条件情報４１から推薦履歴５１を作成しS-205Uに進む。ここで、検索部７０は、作成する推薦履歴５１の条件IDおよびコンテキスト１３に、条件情報４１から得た条件IDおよびユーザ端末１５から受信したコンテキスト１３をおのおの設定する。検索部７０は、推薦履歴５１の推薦成否情報に、「未成功」の情報を設定する。 In S-204U, the search unit 70 creates a recommendation history 51 from the context 13 and the condition information 41, and proceeds to S-205U. Here, the search unit 70 sets the condition ID obtained from the condition information 41 and the context 13 received from the user terminal 15 to the condition ID and context 13 of the recommendation history 51 to be created. The search unit 70 sets “unsuccessful” information in the recommendation success / failure information of the recommendation history 51.
　S-205Uにおいて検索部７０は、S-204Uで生成した推薦履歴５１を推薦履歴記憶部５０に記憶させ、S-206Uに進む。 In S-205U, the search unit 70 stores the recommendation history 51 generated in S-204U in the recommendation history storage unit 50, and proceeds to S-206U.
　S-206Uにおいて検索部７０は、推薦実行装置１７に条件情報４１を送信し、処理を終了する。 In S-206U, the search unit 70 transmits the condition information 41 to the recommendation execution device 17 and ends the process.
　図１９は第２の実施の形態にかかるシステムの動作を示す成功履歴処理フローチャートである。 FIG. 19 is a success history process flowchart showing the operation of the system according to the second embodiment.
　S-201sにおいて成功履歴登録部８０は、成功履歴３１を成功履歴入力端末１６から受信し、S-202sに進む。 In S-201s, the success history registration unit 80 receives the success history 31 from the success history input terminal 16, and proceeds to S-202s.
　S-202sにおいて成功履歴登録部８０は、成功履歴３１から推薦履歴５１の検索要求を作成し、S-203sに進む。推薦履歴５１の検索要求は成功履歴３１に含まれる推薦条件IDおよびユーザIDを含む。 In S-202s, the success history registration unit 80 creates a search request for the recommendation history 51 from the success history 31, and proceeds to S-203s. The search request for the recommendation history 51 includes a recommendation condition ID and a user ID included in the success history 31.
　S-203sにおいて成功履歴登録部８０は、推薦履歴記憶部５０から推薦履歴５１の検索要求と同じ推薦条件ID、ユーザIDを含む推薦履歴５１を検索し、S-204sに進む。 In S-203s, the success history registration unit 80 searches the recommendation history storage unit 50 for the recommendation history 51 including the same recommendation condition ID and user ID as the recommendation history 51 search request, and proceeds to S-204s.
　S-204sにおいて成功履歴登録部８０は、S-203sの結果、合致する推薦履歴５１があればS-205sに進み、なければ-206sに進む。 In S-204s, the success history registration unit 80 proceeds to S-205s if there is a matching recommendation history 51 as a result of S-203s, otherwise proceeds to -206s.
　S-205sにおいて成功履歴登録部８０は、受信した成功履歴３１と同じ推薦条件ID、ユーザIDを含む推薦履歴記憶部５０の推薦履歴５１の推薦成否情報を「成功」に更新し、処理を終了する。 In S-205s, the success history registration unit 80 updates the recommendation success / failure information of the recommendation history 51 of the recommendation history storage unit 50 including the same recommendation condition ID and user ID as the received success history 31 to “success”, and ends the processing. To do.
　S-206sにおいて成功履歴登録部８０６０は、受信した成功履歴３１を成功履歴記憶部３０に記憶させ、処理を終了する。 In S-206s, the success history registration unit 8060 stores the received success history 31 in the success history storage unit 30 and ends the process.
　第２の実施形態の推薦条件修正装置１０は、当該装置自身が自動的に推薦履歴５１および成功履歴３１を蓄積できる。その結果、推薦条件の修正時期の自由度が向上し、管理者の推薦履歴５１および成功履歴３１の作成・維持負担が軽減する。 The recommendation condition correction apparatus 10 according to the second embodiment can automatically store the recommendation history 51 and the success history 31 by the apparatus itself. As a result, the degree of freedom in modifying the recommendation conditions is improved, and the burden of creating and maintaining the manager's recommendation history 51 and success history 31 is reduced.
　その理由は、推薦条件修正装置１０が、管理者端末１１よりシナリオ１２、ユーザ端末１５からコンテキスト１３、成功履歴入力端末１６から成功履歴３１を受信して、推薦履歴５１を作成し、推薦履歴５１および成功履歴３１を蓄積していくからである。 The reason is that the recommendation condition correction apparatus 10 receives the scenario 12 from the administrator terminal 11, the context 13 from the user terminal 15, and the success history 31 from the success history input terminal 16, creates a recommendation history 51, and recommends the recommendation history 51. This is because the success history 31 is accumulated.
　＜第３の実施の形態＞
　次に、本発明の第３の実施の形態について図面を参照して詳細に説明する。 <Third Embodiment>
Next, a third embodiment of the present invention will be described in detail with reference to the drawings.
　図２０は、本発明の第３の実施の形態の推薦条件修正システム９０のブロック図である。図２０が示す推薦条件修正システム９０は、推薦条件修正装置１０と、管理者端末１１と、ユーザ端末１５と、推薦条件修正装置１０、推薦実行装置１７とを含む。 FIG. 20 is a block diagram of a recommendation condition correction system 90 according to the third embodiment of this invention. A recommendation condition correction system 90 illustrated in FIG. 20 includes a recommendation condition correction apparatus 10, an administrator terminal 11, a user terminal 15, a recommendation condition correction apparatus 10, and a recommendation execution apparatus 17.
　本実施の形態の推薦条件修正装置１０は、論理回路、記憶装置等で構成される、登録部６０と、条件記憶部４０と、検索部７０と、推薦履歴記憶部５０と、成功履歴登録部８０と、成功履歴記憶部３０と、推薦条件修正部２０とを含む。推薦条件修正装置１０は、プログラム制御により動作するコンピュータであってもよい。この場合、登録部６０と、条件記憶部４０と、検索部７０と、推薦履歴記憶部５０と、成功履歴登録部８０と、成功履歴記憶部３０と、推薦条件修正部２０は、コンピュータが備える中央処理装置が、記憶装置に格納されたプログラムを読み込んで実行することで実現されてもよい。推薦履歴記憶部５０、条件記憶部４０と成功履歴記憶部３０は、コンピュータが備えるディスク装置等を包含してもよい。 The recommended condition correction apparatus 10 of the present embodiment includes a registration unit 60, a condition storage unit 40, a search unit 70, a recommendation history storage unit 50, and a success history registration unit, which are configured with logic circuits, storage devices, and the like. 80, a success history storage unit 30, and a recommendation condition correction unit 20. The recommended condition correction apparatus 10 may be a computer that operates under program control. In this case, the registration unit 60, the condition storage unit 40, the search unit 70, the recommendation history storage unit 50, the success history registration unit 80, the success history storage unit 30, and the recommendation condition correction unit 20 are provided in the computer. The central processing unit may be realized by reading and executing a program stored in the storage device. The recommendation history storage unit 50, the condition storage unit 40, and the success history storage unit 30 may include a disk device included in the computer.
　本発明の第３の実施の形態における、条件記憶部４０、検索部７０、推薦履歴記憶部５０、成功履歴登録部８０、成功履歴記憶部３０、推薦条件修正部２０は第２の実施の形態と同様である。以下、第２の実施の形態との差分となる、登録部６０、検索部７０、成功履歴登録部８０について説明する。 The condition storage unit 40, the search unit 70, the recommendation history storage unit 50, the success history registration unit 80, the success history storage unit 30, and the recommendation condition correction unit 20 in the third embodiment of the present invention are the same as those in the second embodiment. It is the same. Hereinafter, the registration unit 60, the search unit 70, and the success history registration unit 80, which are differences from the second embodiment, will be described.
　登録部６０は、シナリオ１２を受信し、条件記憶部４０に条件情報４１を登録する。図２１は、シナリオ１２の一例を示す。シナリオ１２はシナリオIDと、推薦条件と、推薦情報と、期待条件とを含む。 The registration unit 60 receives the scenario 12 and registers the condition information 41 in the condition storage unit 40. FIG. 21 shows an example of the scenario 12. The scenario 12 includes a scenario ID, recommendation conditions, recommendation information, and expected conditions.
　シナリオID、推薦条件および推薦情報は、第１の実施の形態と同様の情報である。 Scenario ID, recommendation condition, and recommendation information are the same information as in the first embodiment.
　期待条件は、期待条件IDと条件式を含む情報である。期待条件IDは期待条件を区別するための情報である。条件式は、推薦を行った結果として、ユーザに期待する状態を、コンテキスト１３の一部または全てを用いて表現する。推薦条件修正装置１０は、コンテキスト１３が期待条件に合致するユーザは期待行動をとったと判断する。 Expected condition is information including expected condition ID and conditional expression. The expected condition ID is information for distinguishing the expected condition. The conditional expression expresses a state expected of the user as a result of the recommendation using a part or all of the context 13. The recommended condition correcting apparatus 10 determines that the user whose context 13 matches the expected condition has taken the expected behavior.
　例えば、図２１が示すシナリオ１２の場合、推薦状研修正システム９０は、コンテキスト１３が「位置=駅&年齢=50代&健康状態=良好」に合致するユーザに対して推薦情報1で示される推薦を行う。その結果、コンテキスト１３が「位置=公園」になったユーザは、期待行動をとったと判断される。すなわち、図２１のシナリオ１２における推薦情報１は、ユーザを公園に導くための情報である。 For example, in the case of scenario 12 shown in FIG. 21, the recommended letter training correct system 90 is indicated by recommendation information 1 for a user whose context 13 matches “position = station & age = 50s & health condition = good”. Make a recommendation. As a result, the user whose context 13 is “position = park” is determined to have taken the expected behavior. That is, the recommendation information 1 in the scenario 12 of FIG. 21 is information for guiding the user to the park.
　条件情報４１は、シナリオID、条件ID、条件式、および、条件種別を、含む情報である。第１の実施の形態において、条件情報４１は推薦条件に対応して存在していたが、第３の実施の形態においては、条件情報４１は推薦条件および期待条件の両者に対応して作成される。そして、条件種別は、その条件情報４１が推薦条件および期待条件のいずれに対応しているのかを示す。また、紐付け情報は、シナリオ１２の推薦条件ＩＤが格納される。図２２は、条件情報４１の一例を示す。 The condition information 41 is information including a scenario ID, a condition ID, a conditional expression, and a condition type. In the first embodiment, the condition information 41 exists corresponding to the recommended condition. However, in the third embodiment, the condition information 41 is created corresponding to both the recommended condition and the expected condition. The The condition type indicates whether the condition information 41 corresponds to a recommended condition or an expected condition. In addition, the recommendation condition ID of the scenario 12 is stored as the association information. FIG. 22 shows an example of the condition information 41.
　登録部６０は、シナリオ１２よりシナリオID、推薦条件ID、推薦条件の条件式、推薦情報、期待条件ID、期待条件の条件式を取得する。そして、登録部６０は、「シナリオID=シナリオID、条件ID=推薦条件ID、条件式=推薦条件の条件式、条件種別=推薦条件、紐付け情報=推薦情報ID」とした、条件情報４１を作成する。さらに、登録部６０は、「シナリオID=シナリオID、条件ID=期待条件ID、条件式=期待条件の条件式、条件種別=期待条件、紐付け情報=推薦条件ID」とした条件情報４１も作成する。 The registration unit 60 acquires a scenario ID, a recommendation condition ID, a recommendation condition conditional expression, recommendation information, an expected condition ID, and an expected condition conditional expression from the scenario 12. Then, the registration unit 60 sets the condition information 41 as “scenario ID = scenario ID, condition ID = recommended condition ID, conditional expression = conditional condition of recommended condition, condition type = recommended condition, association information = recommended information ID”. Create Furthermore, the registration unit 60 also includes condition information 41 with “scenario ID = scenario ID, condition ID = expected condition ID, conditional expression = conditional condition of expected condition, condition type = expected condition, association information = recommended condition ID”. create.
　条件記憶部４０は、１以上の条件情報４１を記憶する。図２２は、条件記憶部４０に記憶される複数の条件情報４１の一例を示す。図２２は、推薦条件１、推薦条件２、期待条件1、期待条件2という4つの条件情報４１を条件記憶部４０が記憶する場合の例を示す。 The condition storage unit 40 stores one or more condition information 41. FIG. 22 shows an example of a plurality of condition information 41 stored in the condition storage unit 40. FIG. 22 shows an example in which the condition storage unit 40 stores four condition information 41 including recommendation condition 1, recommendation condition 2, expectation condition 1, and expectation condition 2.
　検索部７０は、ユーザ端末１５からコンテキスト１３を受信し、条件記憶部４０から当該コンテキスト１３に合致する条件情報４１を取得する。合致する条件情報４１の条件種別が推薦条件の場合、検索部７０は、推薦履歴５１を作成するとともに、条件情報４１を推薦実行装置１７に送信する。条件種別が期待条件の場合、検索部７０は、コンテキスト１３と条件情報４１を成功履歴登録部８０に送信する。 The search unit 70 receives the context 13 from the user terminal 15 and acquires the condition information 41 that matches the context 13 from the condition storage unit 40. When the condition type of the matching condition information 41 is the recommendation condition, the search unit 70 creates the recommendation history 51 and transmits the condition information 41 to the recommendation execution device 17. When the condition type is an expected condition, the search unit 70 transmits the context 13 and the condition information 41 to the success history registration unit 80.
　例えば、図２２が示す複数の条件情報４１が条件記憶部４０に記憶されている場合、検索部７０は、図２３Ａが示すコンテキスト１３を受信すると、コンテキスト１３が「位置＝駅、年齢＝50代、性別＝男性、歩数＝１０００、健康状態＝良好」に合致する条件情報４１を取得する。 For example, when a plurality of condition information 41 shown in FIG. 22 is stored in the condition storage unit 40, when the search unit 70 receives the context 13 shown in FIG. 23A, the context 13 is “location = station, age = 50s”. , Condition information 41 matching “sex = male, number of steps = 1000, health condition = good” is acquired.
　この場合、取得される条件情報４１は、条件IDが推薦条件1、シナリオIDがシナリオ1、条件式が「位置=駅 & 年齢=50代 & 健康状態=良好」、紐付け情報が推薦情報1の条件情報４１である。条件情報４１の条件種別が推薦条件であるため、検索部７０は、推薦履歴５１を作成するとともに、推薦実行装置１７に条件情報４１を送信する。推薦実行装置１７は、推薦を実行する。 In this case, the acquired condition information 41 is that the condition ID is the recommended condition 1, the scenario ID is the scenario 1, the conditional expression is “position = station & age = 50's & health condition = good”, and the linking information is the recommendation information 1 Condition information 41. Since the condition type of the condition information 41 is a recommendation condition, the search unit 70 creates the recommendation history 51 and transmits the condition information 41 to the recommendation execution device 17. The recommendation execution device 17 executes recommendation.
　検索部７０は、図２３Ｂが示すコンテキスト１３を受信すると、コンテキスト１３が「位置＝公園、年齢＝50代、性別＝男性、歩数＝３０００、健康状態＝良好」に合致する条件情報４１を取得する。この場合、取得される条件情報４１は、条件IDが期待条件1、シナリオIDがシナリオ1、条件式が「位置=公園」紐付け情報が推薦条件1の条件情報４１である。条件情報４１の条件種別が期待条件であるため、成功履歴登録部８０に条件情報４１およびコンテキスト１３を送信する。 When the search unit 70 receives the context 13 shown in FIG. 23B, the search unit 70 acquires condition information 41 that matches the “position = park, age = 50s, gender = male, step count = 3000, health = good”. . In this case, the acquired condition information 41 is the condition information 41 where the condition ID is the expected condition 1, the scenario ID is the scenario 1, and the conditional expression is “position = park”. Since the condition type of the condition information 41 is an expected condition, the condition information 41 and the context 13 are transmitted to the success history registration unit 80.
　成功履歴登録部８０は、条件情報４１とコンテキスト１３を受信し、条件情報４１とコンテキスト１３から成功履歴３１を生成する。成功履歴登録部８０は、条件情報４１から抽出した紐付け情報（推薦条件ＩＤ）とコンテキスト１３から成功履歴３１を生成する。 The success history registration unit 80 receives the condition information 41 and the context 13 and generates the success history 31 from the condition information 41 and the context 13. The success history registration unit 80 generates the success history 31 from the association information (recommended condition ID) extracted from the condition information 41 and the context 13.
　図２４が示す条件情報４１と図２５が示すコンテキスト１３を受信した場合、成功履歴登録部８０は、条件情報４１から紐付け情報（推薦条件ＩＤ）の推薦条件1を抽出し、コンテキスト１３と合わせて図２６が示す成功履歴３１を作成する。 When the condition information 41 shown in FIG. 24 and the context 13 shown in FIG. 25 are received, the success history registration unit 80 extracts the recommendation condition 1 of the association information (recommended condition ID) from the condition information 41 and matches it with the context 13. 26 creates the success history 31 shown in FIG.
　次に、フローチャートを参照して本実施の形態の全体の動作について詳細に説明する。本実施の形態のシステムの動作は、シナリオ１２を登録するシナリオ登録フローと、ユーザのコンテキスト１３を受信する度に行うユーザコンテキスト処理フロー、推薦条件の修正を行う推薦条件修正フローに分けることができる。 Next, the overall operation of the present embodiment will be described in detail with reference to a flowchart. The operation of the system according to the present embodiment can be divided into a scenario registration flow for registering a scenario 12, a user context processing flow performed every time a user context 13 is received, and a recommended condition correction flow for correcting a recommendation condition. .
　シナリオ登録フローと推薦条件修正フローは、第１の実施のシステムと同様である。以下、ユーザコンテキスト処理フローについて説明する。 The scenario registration flow and recommendation condition correction flow are the same as those in the first implementation system. Hereinafter, the user context processing flow will be described.
　図２７は第３の実施の形態にかかるシステムの動作を示すコンテキスト処理フローチャートである。 FIG. 27 is a context processing flowchart showing the operation of the system according to the third embodiment.
　S-301Uにおいて検索部７０は、ユーザのコンテキスト１３を受信し、S-302Uに進む。 In S-301U, the search unit 70 receives the user context 13 and proceeds to S-302U.
　S-302Uにおいて検索部７０は、S-301Uで受信したコンテキスト１３に合致する条件情報４１を条件記憶部４０から検索し、S-303Uに進む。 In S-302U, the search unit 70 searches the condition storage unit 40 for the condition information 41 that matches the context 13 received in S-301U, and proceeds to S-303U.
　S-303Uにおいて検索部７０は、S-302Uの検索の結果、合致する条件情報４１の条件種別が推薦条件の場合S-304Uに進み、そうでなければS-307Uに進む。 In S-303U, the search unit 70 proceeds to S-304U if the condition type of the matching condition information 41 is the recommended condition as a result of the search in S-302U, and proceeds to S-307U otherwise.
　S-304Uにおいて検索部７０は、コンテキスト１３および条件情報４１から推薦履歴５１を作成しS-305Uに進む。ここで、検索部７０は、作成する推薦履歴５１の条件IDおよびコンテキスト１３に、条件情報４１から得た条件IDおよびユーザ端末１５から受信したコンテキスト１３をおのおの設定する。検索部７０は、推薦履歴５１の推薦成否情報に、「未成功」の情報を設定する。 In S-304U, the search unit 70 creates a recommendation history 51 from the context 13 and the condition information 41, and proceeds to S-305U. Here, the search unit 70 sets the condition ID obtained from the condition information 41 and the context 13 received from the user terminal 15 to the condition ID and context 13 of the recommendation history 51 to be created. The search unit 70 sets “unsuccessful” information in the recommendation success / failure information of the recommendation history 51.
　S-305Uにおいて検索部７０は、S-304Uで生成した推薦履歴５１を推薦履歴記憶部５０に記憶させ、S-306Uに進む。 In S-305U, the search unit 70 stores the recommendation history 51 generated in S-304U in the recommendation history storage unit 50, and proceeds to S-306U.
　S-306Uにおいて検索部７０は、推薦実行装置１７に条件情報４１を送信し、処理を終了する。 In S-306U, the search unit 70 transmits the condition information 41 to the recommendation execution device 17 and ends the process.
　S-307Uにおいて検索部７０は、S-302Uの検索の結果、合致する条件情報４１の条件種別が期待条件の場合、コンテキスト１３および条件情報４１を成功履歴登録部８０に送信してS-308Uに進み、合致する条件情報４１が無ければ処理を終了する。 In S-307U, if the condition type of the matching condition information 41 is an expected condition as a result of the search in S-302U, the search unit 70 transmits the context 13 and the condition information 41 to the success history registration unit 80, and the S-308U If there is no matching condition information 41, the process is terminated.
　S-308Uにおいて成功履歴登録部８０は、受信したコンテキスト１３および条件情報４１から成功履歴３１を生成し、S-309sに進む。成功履歴登録部８０は、条件情報４１から抽出した紐付け情報とコンテキスト１３から成功履歴３１を生成する。 In S-308U, the success history registration unit 80 generates a success history 31 from the received context 13 and condition information 41, and proceeds to S-309s. The success history registration unit 80 generates the success history 31 from the association information extracted from the condition information 41 and the context 13.
　S-309Uにおいて成功履歴登録部８０は、成功履歴３１から推薦履歴検索要求を作成し、S-310sに進む。推薦履歴検索要求は成功履歴３１に含まれる推薦条件IDおよびユーザIDを含む。 In S-309U, the success history registration unit 80 creates a recommendation history search request from the success history 31, and proceeds to S-310s. The recommendation history search request includes a recommendation condition ID and a user ID included in the success history 31.
　S-310Uにおいて成功履歴登録部８０は、推薦履歴記憶部５０から推薦履歴検索要求と同じ推薦条件ID、ユーザIDを含む条件情報４１を検索し、S-311Uに進む。 In S-310U, the success history registration unit 80 searches the recommendation history storage unit 50 for the condition information 41 including the same recommendation condition ID and user ID as the recommendation history search request, and proceeds to S-311U.
　S-311Uにおいて成功履歴登録部８０は、S-310Uの結果、合致する条件情報４１があればS-312Uに進み、なければS-313Uに進む。 In S-311U, the success history registration unit 80 proceeds to S-312U if there is matching condition information 41 as a result of S-310U, and proceeds to S-313U if not.
　S-312Uにおいて成功履歴登録部８０は、成功履歴３１と同じ推薦条件ID、ユーザIDを含む推薦履歴記憶部５０の推薦履歴５１の推薦成否情報を「成功」に更新し、処理を終了する。 In S-312U, the success history registration unit 80 updates the recommendation success / failure information of the recommendation history 51 of the recommendation history storage unit 50 including the same recommendation condition ID and user ID as the success history 31 to “success”, and ends the process.
　S-313Uにおいて成功履歴登録部８０は、成功履歴３１を成功履歴記憶部３０に記憶させ、処理を終了する。 　第３の実施の推薦条件修正装置１０は、当該装置自身が自動的に推薦履歴５１および成功履歴３１を蓄積できる。その結果、推薦条件の修正時期の自由度が向上し、管理者等の推薦履歴５１および成功履歴３１の作成・維持負担が軽減する。さらに、成功履歴入力端末１６から成功履歴３１を入力する必要がなくなり、一層、管理者等の推薦履歴５１および成功履歴３１の作成・維持負担が軽減する。 In S-313U, the success history registration unit 80 stores the success history 31 in the success history storage unit 30 and ends the process.推薦 The recommendation condition correcting apparatus 10 of the third implementation can automatically store the recommendation history 51 and the success history 31 by the apparatus itself. As a result, the degree of freedom in modifying the recommendation conditions is improved, and the burden of creating / maintaining the recommendation history 51 and the success history 31 of the manager or the like is reduced. Furthermore, it is not necessary to input the success history 31 from the success history input terminal 16, and the burden of creating and maintaining the recommendation history 51 and the success history 31 for the manager and the like is further reduced.
　その理由は、推薦条件修正装置１０が、管理者端末１１よりシナリオ１２、ユーザ端末１５からコンテキスト１３を受信して、推薦履歴５１を作成し、推薦履歴５１および成功履歴３１を蓄積していくからである。 The reason is that the recommendation condition correction apparatus 10 receives the scenario 12 from the administrator terminal 11 and the context 13 from the user terminal 15, creates the recommendation history 51, and accumulates the recommendation history 51 and the success history 31. It is.
　＜第４の実施の形態＞
　次に、本発明の第４の実施の形態について図面を参照して詳細に説明する。 <Fourth embodiment>
Next, a fourth embodiment of the present invention will be described in detail with reference to the drawings.
　図２８は、本発明の第４の実施の形態の推薦条件修正装置１０のブロック図である。本実施の形態の推薦条件修正装置１０は、条件記憶部４０と、推薦履歴記憶部５０と、成功履歴記憶部３０と、推薦条件修正部２０を備える。 FIG. 28 is a block diagram of the recommended condition correcting apparatus 10 according to the fourth embodiment of this invention. The recommended condition correction apparatus 10 of the present embodiment includes a condition storage unit 40, a recommendation history storage unit 50, a success history storage unit 30, and a recommendation condition correction unit 20.
　条件記憶部４０は、複数の属性を包含する、ユーザ情報であるコンテキスト１３の１以上の属性の値の範囲を指定する推薦条件を格納する。推薦履歴記憶部５０は、コンテキスト１３が推薦条件を満たすことによって、所定の行動を促す推薦情報を出力されたユーザである対象ユーザのおのおの対応に、当該対象ユーザのコンテキスト１３の各属性の値および当該対象ユーザが所定の行動をとったか否かを示す成否情報を含む推薦履歴５１を格納する。成功履歴記憶部３０は、対象ユーザ以外で所定の行動をとったユーザである成功ユーザのおのおの対応に、当該成功ユーザのコンテキスト１３の各属性の値を格納する。 The condition storage unit 40 stores a recommendation condition that specifies a range of values of one or more attributes of the context 13 that is user information including a plurality of attributes. The recommendation history storage unit 50 responds to each target user who is a user who has output recommendation information for encouraging a predetermined action by satisfying the recommendation condition of the context 13, and the value of each attribute of the target user context 13 and A recommendation history 51 including success / failure information indicating whether or not the target user has taken a predetermined action is stored. The success history storage unit 30 stores the value of each attribute of the context 13 of the successful user in response to each successful user who is a user who has taken a predetermined action other than the target user.
　推薦条件修正部２０は、推薦履歴記憶部５０に格納されているすべての推薦履歴５１のコンテキスト１３に含まれる各属性の各値対応に、コンテキスト１３の当該属性が当該値である推薦履歴５１のうち、所定の行動をとったことを示す推薦履歴５１の割合を示す第１寄与率を算出する。さらに、推薦条件修正部２０は、推薦条件内の各属性対応に、成功履歴３１のうち、コンテキスト１３の当該属性の値が推薦条件と一致する成功履歴３１の割合を示す第２寄与率を算出し、第１寄与率および第２寄与率に基づいて推薦条件を修正する。 The recommendation condition modification unit 20 corresponds to each value of each attribute included in the context 13 of all the recommendation histories 51 stored in the recommendation history storage unit 50, and the recommendation history 51 of the recommendation history 51 in which the attribute of the context 13 is the value. Of these, a first contribution ratio indicating the ratio of the recommendation history 51 indicating that a predetermined action has been taken is calculated. Further, the recommendation condition correction unit 20 calculates a second contribution ratio indicating the ratio of the success history 31 in which the value of the attribute of the context 13 matches the recommendation condition in the success history 31 for each attribute in the recommendation condition. Then, the recommendation condition is corrected based on the first contribution rate and the second contribution rate.
　本発明によれば、推薦条件を効果的に修正することができる推薦条件修正装置１０を実現できる。例えば、当該装置は、「推薦成功率」の低下を抑えつつ、「推薦成功数」の向上を図ることが可能となる。その理由は、推薦条件修正装置１０は、成功履歴記憶部３０に蓄積された成功履歴３１を用いて削除属性情報判定を行うからである。 According to the present invention, it is possible to realize the recommended condition correcting apparatus 10 that can effectively correct the recommended conditions. For example, the device can improve the “number of successful recommendations” while suppressing a decrease in the “recommendation success rate”. The reason is that the recommendation condition correction apparatus 10 performs the deletion attribute information determination using the success history 31 accumulated in the success history storage unit 30.
　以上、実施形態（及び実施例）を参照して本願発明を説明したが、本願発明は上記実施形態に限定されものではない。本願発明の構成や詳細には、本願発明のスコープ内で当業者が理解し得る様々な変更をすることができる。 The present invention has been described above with reference to the embodiments (and examples), but the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.
　本発明は、広告配信システム、制御システム、通知システム、エキスパートシステム、ナビシステム等に代表される推薦条件修正システム９０に適用できる。 The present invention can be applied to a recommended condition correction system 90 represented by an advertisement distribution system, a control system, a notification system, an expert system, a navigation system, and the like.
　この出願は、２０１１年１２月１５日に出願された日本出願特願２０１１－２７４７９３を基礎とする優先権を主張し、その開示の全てをここに取り込む。 This application claims priority based on Japanese Patent Application No. 2011-274793 filed on Dec. 15, 2011, the entire disclosure of which is incorporated herein.
　１０　　推薦条件修正装置
　１１　　管理者端末
　１５　　ユーザ端末
　１６　　成功履歴入力端末
　１７　　推薦実行装置
　２０　　推薦条件修正部
　３０　　成功履歴記憶部
　３１　　成功履歴
　４０　　条件記憶部
　４１　　条件情報
　５０　　推薦履歴記憶部
　５１　　推薦履歴
　６０　　登録部
　７０　　検索部
　８０　　成功履歴登録部
　９０　　推薦条件修正システム DESCRIPTION OF SYMBOLS 10 Recommendation condition correction apparatus 11 Administrator terminal 15 User terminal 16 Success history input terminal 17 Recommendation execution apparatus 20 Recommendation condition correction part 30 Success history memory | storage part 31 Success history 40 Condition memory | storage part 41 Condition information 50 Recommendation history memory | storage part 51 Recommendation history 60 Registration unit 70 Search unit 80 Success history registration unit 90 Recommended condition correction system

Claims (18)

  1. 　複数の属性を包含する、ユーザ情報であるコンテキストの１以上の前記属性の値の範囲を指定する推薦条件を格納する条件記憶手段と、
    　前記コンテキストが前記推薦条件を満たすことによって、所定の行動を促す推薦情報を出力されたユーザである対象ユーザのおのおの対応に、当該対象ユーザの前記コンテキストの各前記属性の値および当該対象ユーザが前記所定の行動をとったか否かを示す成否情報を含む推薦履歴を格納する推薦履歴記憶手段と、
    　前記対象ユーザ以外で前記所定の行動をとったユーザである成功ユーザのおのおの対応に、当該成功ユーザの前記コンテキストの各前記属性の値を格納する成功履歴記憶手段と、
    　前記推薦履歴記憶手段に格納されているすべての前記推薦履歴の前記コンテキストに含まれる各前記属性の各値対応に、前記コンテキストの当該属性が当該値である前記推薦履歴のうち、前記所定の行動をとったことを示す前記推薦履歴の割合を示す第１寄与率を算出し、
    前記推薦条件内の各前記属性対応に、前記成功履歴のうち、前記コンテキストの当該属性の値が前記推薦条件と一致する前記成功履歴の割合を示す第２寄与率を算出し、前記第１寄与率および前記第２寄与率に基づいて前記推薦条件を修正する推薦条件修正手段を、備える推薦条件修正装置。     Condition storage means for storing a recommendation condition that specifies a range of one or more values of the attribute that is user information including a plurality of attributes;
    When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action. A recommendation history storage means for storing a recommendation history including success / failure information indicating whether or not a predetermined action has been taken;
    Success history storage means for storing the value of each attribute of the context of the successful user in response to each successful user who is a user who has taken the predetermined action other than the target user;
    Corresponding to each value of each attribute included in the context of all the recommendation histories stored in the recommendation history storage unit, the predetermined action in the recommendation history in which the attribute of the context is the value Calculating a first contribution ratio indicating a ratio of the recommendation history indicating that
    For each attribute in the recommendation condition, a second contribution ratio indicating a ratio of the success history in which the attribute value of the context matches the recommendation condition in the success history is calculated, and the first contribution A recommendation condition correction device comprising recommendation condition correction means for correcting the recommendation condition based on the rate and the second contribution rate.
  2. 　前記推薦条件修正手段は、前記第１寄与率が第１の追加閾値以上の前記属性の値を前記推薦条件に追加し、前記第２寄与率が第１の削除閾値以下の前記属性の値を前記推薦条件から削除する、請求項１の推薦条件修正装置。 The recommendation condition correcting means adds the value of the attribute having the first contribution rate equal to or higher than a first addition threshold to the recommendation condition, and sets the value of the attribute whose second contribution rate is equal to or lower than a first deletion threshold. The recommendation condition correction apparatus according to claim 1, wherein the recommendation condition correction apparatus is deleted from the recommendation condition.
  3. 前記推薦条件修正手段は、前記推薦履歴のうち、前記所定の行動をとったことを示す推薦履歴の割合である成功率を算出し、前記成功率が第２の削除閾値以下である場合、前記第２寄与率が前記第１の削除閾値以下の前記属性の値を前記推薦条件から削除する、請求項２の推薦条件修正装置。 The recommendation condition correction means calculates a success rate that is a ratio of a recommendation history indicating that the predetermined action is taken out of the recommendation history, and when the success rate is equal to or less than a second deletion threshold, The recommendation condition correction apparatus according to claim 2, wherein a value of the attribute having a second contribution rate equal to or less than the first deletion threshold is deleted from the recommendation condition.
  4. 　前記推薦条件修正手段は、前記第１寄与率が前記第１の追加閾値以上、かつ、前記第２寄与率が第２の追加閾値以上である前記属性の値を前記推薦条件に追加する、請求項２または３の推薦条件修正装置。 The recommendation condition correction means adds the value of the attribute having the first contribution rate equal to or higher than the first additional threshold value and the second contribution rate equal to or higher than a second additional threshold value to the recommendation condition. Item 2 or 3 recommendation condition correction device.
  5. 　前記コンテキストは、前記属性の一つとしてユーザ識別子を包含し、
    　少なくとも一部をユーザ端末から受信した、ユーザの前記コンテキストが前記推薦条件を満たす場合に、前記推薦条件を満たすと判断された前記コンテキストと前記ユーザが前記所定の行動をとっていないことを示す前記成否情報を前記推薦履歴として前記推薦履歴記憶手段に格納する検索手段と、
    　成功履歴入力端末から、成功ユーザの前記コンテキストを取得して、前記推薦履歴記憶部に、取得された当該コンテキストが含む前記ユーザ識別子と一致する前記ユーザ識別子を含む前記推薦履歴が存在するかを検索し、
    存在すれば、検索された前記推薦履歴の前記成否情報を前記対象ユーザが前記所定の行動をとったことを示すように更新し、存在しなければ、前記成功履歴入力端末から取得された前記コンテキストを前記成功履歴記憶部に格納する成功履歴登録手段を、さらに備えた、請求項１乃至４のいずれかに記載の推薦条件修正装置。     The context includes a user identifier as one of the attributes,
    When the context of the user who has received at least a part from the user terminal satisfies the recommendation condition, the context determined to satisfy the recommendation condition and the user does not take the predetermined action Search means for storing success / failure information as the recommendation history in the recommendation history storage means;
    Obtain the context of a successful user from a success history input terminal, and search in the recommendation history storage unit whether the recommendation history including the user identifier that matches the user identifier included in the acquired context exists. And
    If it exists, the success / failure information of the retrieved recommendation history is updated to indicate that the target user has taken the predetermined action, and if not, the context acquired from the success history input terminal The recommendation condition correcting device according to claim 1, further comprising a success history registration unit that stores the success history in the success history storage unit.
  6. 　前記条件記憶手段は、前記推薦条件に加え、ユーザが前記所定の行動をとったことを判別するために前記コンテキストから選択された、１以上の前記属性の値が満たすべき条件を含む期待条件を格納し、
    　前記コンテキストは、前記属性の一つとしてユーザ識別子を包含し、
    　少なくとも一部を前記ユーザ端末から取得した、ユーザの前記コンテキストが前記推薦条件を満たす場合に、取得された前記コンテキストと前記ユーザが前記所定の行動をとっていないことを示す前記成否情報を前記推薦履歴として前記推薦履歴記憶手段に格納する検索手段と、
    　少なくとも一部を前記ユーザ端末から取得した、前記ユーザの前記コンテキストが前記期待条件を満たす場合に、取得された当該コンテキストが含む前記ユーザ識別子と一致する前記ユーザ識別子を含む前記推薦履歴が存在するかを検索し、存在すれば、検索された前記推薦履歴の前記成否情報を前記対象ユーザが前記所定の行動をとったことを示すように更新し、存在しなければ、取得された前記コンテキストを前記成功履歴記憶部に格納する成功履歴登録手段を、さらに備えた、請求項１乃至４のいずれかに記載の推薦条件修正装置。     In addition to the recommendation condition, the condition storage means includes an expectation condition including a condition to be satisfied by one or more attribute values selected from the context in order to determine that the user has taken the predetermined action Store and
    The context includes a user identifier as one of the attributes,
    When the user's context that has acquired at least a part from the user terminal satisfies the recommendation condition, the acquired context and the success / failure information indicating that the user has not taken the predetermined action are recommended. Search means for storing in the recommendation history storage means as a history;
    Whether or not the recommendation history including the user identifier that matches the user identifier included in the acquired context exists when the context of the user acquired at least a part from the user terminal satisfies the expected condition If it exists, the success / failure information of the searched recommendation history is updated so as to indicate that the target user has taken the predetermined action. The recommendation condition correction device according to claim 1, further comprising a success history registration unit that stores the success history in the success history storage unit.
  7. 　複数の属性を包含する、ユーザ情報であるコンテキストの１以上の前記属性の値の範囲を指定する推薦条件を条件記憶手段に格納し、
    　前記コンテキストが前記推薦条件を満たすことによって、所定の行動を促す推薦情報を出力されたユーザである対象ユーザのおのおの対応に、当該対象ユーザの前記コンテキストの各前記属性の値および当該対象ユーザが前記所定の行動をとったか否かを示す成否情報を含む推薦履歴を推薦履歴記憶手段に格納し、
    　前記対象ユーザ以外で前記所定の行動をとったユーザである成功ユーザのおのおの対応に、当該成功ユーザの前記コンテキストの各前記属性の値を成功履歴記憶手段に格納し、前記推薦履歴記憶手段に格納されているすべての前記推薦履歴の前記コンテキストに含まれる各前記属性の各値対応に、前記コンテキストの当該属性が当該値である前記推薦履歴のうち、前記所定の行動をとったことを示す前記推薦履歴の割合を示す第１寄与率を算出し、
    前記推薦条件内の各前記属性対応に、前記成功履歴のうち、前記コンテキストの当該属性の値が前記推薦条件と一致する前記成功履歴の割合を示す第２寄与率を算出し、前記第１寄与率および前記第２寄与率に基づいて前記推薦条件を修正する、推薦条件修正方法。     Storing a recommendation condition that specifies a range of one or more values of the attribute that is user information including a plurality of attributes in the condition storage unit;
    When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action. Storing a recommendation history including success / failure information indicating whether or not a predetermined action has been taken in the recommendation history storage means;
    In response to each successful user who is the user who has taken the predetermined action other than the target user, the value of each attribute of the context of the successful user is stored in a success history storage unit, and stored in the recommendation history storage unit The value indicating that the attribute of the context is the value corresponding to each value of the attribute included in the context of all of the recommendation histories being performed indicates that the predetermined action has been taken in the recommendation history Calculate the first contribution ratio indicating the ratio of recommendation history,
    For each attribute in the recommendation condition, a second contribution ratio indicating a ratio of the success history in which the attribute value of the context matches the recommendation condition in the success history is calculated, and the first contribution A recommendation condition correction method for correcting the recommendation condition based on the rate and the second contribution rate.
  8. 　前記第１寄与率が第１の追加閾値以上の前記属性の値を前記推薦条件に追加し、前記第２寄与率が第１の削除閾値以下の前記属性の値を前記推薦条件から削除する、請求項７の推薦条件修正方法。 A value of the attribute having the first contribution rate equal to or higher than a first addition threshold is added to the recommendation condition, and a value of the attribute having the second contribution rate equal to or lower than a first deletion threshold is deleted from the recommendation condition; The recommendation condition correction method according to claim 7.
  9. 　前記推薦履歴のうち、前記所定の行動をとったことを示す推薦履歴の割合である成功率を算出し、前記成功率が第２の削除閾値以下である場合、前記第２寄与率が前記第１の削除閾値以下の前記属性の値を前記推薦条件から削除する、請求項８の推薦条件修正方法。 A success rate that is a ratio of a recommendation history indicating that the predetermined action is taken out of the recommendation history is calculated, and when the success rate is equal to or less than a second deletion threshold, the second contribution rate is the first contribution rate. The recommendation condition correcting method according to claim 8, wherein a value of the attribute equal to or less than one deletion threshold is deleted from the recommendation condition.
  10. 　前記第１寄与率が前記第１の追加閾値以上、かつ、前記第２寄与率が第２の追加閾値以上である前記属性の値を前記推薦条件に追加する、請求項７または８の推薦条件修正方法。 The recommendation condition according to claim 7 or 8, wherein a value of the attribute having the first contribution rate equal to or higher than the first additional threshold value and the second contribution rate equal to or higher than a second additional threshold value is added to the recommendation condition. How to fix.
  11. 　前記コンテキストは、前記属性の一つとしてユーザ識別子を包含し、
    　少なくとも一部をユーザ端末から受信した、ユーザの前記コンテキストが前記推薦条件を満たす場合に、前記推薦条件を満たすと判断された前記コンテキストと前記ユーザが前記所定の行動をとっていないことを示す前記成否情報を前記推薦履歴として前記推薦履歴記憶手段に格納し、
    　成功履歴入力端末から、成功ユーザの前記コンテキストを取得して、前記推薦履歴記憶部に、取得された当該コンテキストが含む前記ユーザ識別子と一致する前記ユーザ識別子を含む前記推薦履歴が存在するかを検索し、
    存在すれば、検索された前記推薦履歴の前記成否情報を前記対象ユーザが前記所定の行動をとったことを示すように更新し、存在しなければ、前記成功履歴入力端末から取得された前記コンテキストを前記成功履歴記憶部に格納する、請求項７乃至１０のいずれかに記載の推薦条件修正方法。     The context includes a user identifier as one of the attributes,
    When the context of the user who has received at least a part from the user terminal satisfies the recommendation condition, the context determined to satisfy the recommendation condition and the user does not take the predetermined action Storing success / failure information as the recommendation history in the recommendation history storage means;
    Obtain the context of a successful user from a success history input terminal, and search in the recommendation history storage unit whether the recommendation history including the user identifier that matches the user identifier included in the acquired context exists. And
    If it exists, the success / failure information of the retrieved recommendation history is updated to indicate that the target user has taken the predetermined action, and if not, the context acquired from the success history input terminal The recommendation condition correcting method according to claim 7, wherein: is stored in the success history storage unit.
  12. 　前記条件記憶手段に、前記推薦条件に加え、ユーザが前記所定の行動をとったことを判別するために前記コンテキストから選択された、１以上の前記属性の値が満たすべき条件を含む期待条件を格納し、
    　前記コンテキストは、前記属性の一つとしてユーザ識別子を包含し、
    　少なくとも一部を前記ユーザ端末から取得した、ユーザの前記コンテキストが前記推薦条件を満たす場合に、取得された前記コンテキストと前記ユーザが前記所定の行動をとっていないことを示す前記成否情報を前記推薦履歴として前記推薦履歴記憶手段に格納し、
    　少なくとも一部を前記ユーザ端末から取得した、前記ユーザの前記コンテキストが前記期待条件を満たす場合に、取得された当該コンテキストが含む前記ユーザ識別子と一致する前記ユーザ識別子を含む前記推薦履歴が存在するかを検索し、存在すれば、検索された前記推薦履歴の前記成否情報を前記対象ユーザが前記所定の行動をとったことを示すように更新し、存在しなければ、取得された前記コンテキストを前記成功履歴記憶部に格納する、請求項７乃至１０のいずれかに記載の推薦条件修正方法。     In the condition storage means, in addition to the recommendation condition, an expectation condition including a condition to be satisfied by one or more values of the attribute selected from the context in order to determine that the user has taken the predetermined action Store and
    The context includes a user identifier as one of the attributes,
    When the user's context that has acquired at least a part from the user terminal satisfies the recommendation condition, the acquired context and the success / failure information indicating that the user has not taken the predetermined action are recommended. Stored in the recommendation history storage means as a history,
    Whether or not the recommendation history including the user identifier that matches the user identifier included in the acquired context exists when the context of the user acquired at least a part from the user terminal satisfies the expected condition If it exists, the success / failure information of the searched recommendation history is updated so as to indicate that the target user has taken the predetermined action. The recommendation condition correction method according to claim 7, wherein the recommendation condition correction method is stored in the success history storage unit.
  13. 　複数の属性を包含する、ユーザ情報であるコンテキストの１以上の前記属性の値の範囲を指定する推薦条件を条件記憶手段格納する条件記憶処理と、
    　前記コンテキストが前記推薦条件を満たすことによって、所定の行動を促す推薦情報を出力されたユーザである対象ユーザのおのおの対応に、当該対象ユーザの前記コンテキストの各前記属性の値および当該対象ユーザが前記所定の行動をとったか否かを示す成否情報を含む推薦履歴を推薦履歴記憶手段に格納する推薦履歴記憶処理と、
    　前記対象ユーザ以外で前記所定の行動をとったユーザである成功ユーザのおのおの対応に、当該成功ユーザの前記コンテキストの各前記属性の値を成功履歴記憶手段に格納する処理と、
    前記推薦履歴記憶手段に格納されているすべての前記推薦履歴の前記コンテキストに含まれる各前記属性の各値対応に、前記コンテキストの当該属性が当該値である前記推薦履歴のうち、前記所定の行動をとったことを示す前記推薦履歴の割合を示す第１寄与率を算出し、
    前記推薦条件内の各前記属性対応に、前記成功履歴のうち、前記コンテキストの当該属性の値が前記推薦条件と一致する前記成功履歴の割合を示す第２寄与率を算出し、前記第１寄与率および前記第２寄与率に基づいて前記推薦条件を修正する推薦条件修正処理を、コンピュータに実行させる推薦条件修正プログラム。     A condition storage process that stores a recommendation condition that specifies a range of one or more values of the attribute that is user information, including a plurality of attributes, as condition storage means;
    When the context satisfies the recommendation condition, each attribute value of the target user and the target user correspond to each target user who is a user who has output recommendation information for encouraging a predetermined action. A recommendation history storage process for storing a recommendation history including success / failure information indicating whether or not a predetermined action has been taken in the recommendation history storage means;
    A process of storing the value of each attribute of the context of the successful user in a success history storage unit in response to each successful user who is a user who has taken the predetermined action other than the target user;
    Corresponding to each value of each attribute included in the context of all the recommendation histories stored in the recommendation history storage unit, the predetermined action in the recommendation history in which the attribute of the context is the value Calculating a first contribution ratio indicating a ratio of the recommendation history indicating that
    For each attribute in the recommendation condition, a second contribution ratio indicating a ratio of the success history in which the attribute value of the context matches the recommendation condition in the success history is calculated, and the first contribution A recommendation condition correction program for causing a computer to execute a recommendation condition correction process for correcting the recommendation condition based on the rate and the second contribution rate.
  14. 　前記第１寄与率が第１の追加閾値以上の前記属性の値を前記推薦条件に追加し、前記第２寄与率が第１の削除閾値以下の前記属性の値を前記推薦条件から削除する前記推薦条件修正処理を、前記コンピュータに実行させる、請求項１３の推薦条件修正プログラム。 The attribute value having the first contribution rate equal to or higher than a first addition threshold is added to the recommendation condition, and the attribute value having the second contribution rate equal to or less than a first deletion threshold is deleted from the recommendation condition. 14. The recommended condition correction program according to claim 13, which causes the computer to execute a recommended condition correction process.
  15. 　前記推薦履歴のうち、前記所定の行動をとったことを示す推薦履歴の割合である成功率を算出し、前記成功率が第２の削除閾値以下である場合、前記第２寄与率が前記第１の削除閾値以下の前記属性の値を前記推薦条件から削除する前記推薦条件修正処理を、前記コンピュータに実行させる、請求項１４の推薦条件修正プログラム。 A success rate that is a ratio of a recommendation history indicating that the predetermined action is taken out of the recommendation history is calculated, and when the success rate is equal to or less than a second deletion threshold, the second contribution rate is the first contribution rate. The recommendation condition correction program according to claim 14, wherein the computer executes the recommendation condition correction processing for deleting the attribute value equal to or less than one deletion threshold from the recommendation condition.
  16. 　前記第１寄与率が前記第１の追加閾値以上、かつ、前記第２寄与率が第２の追加閾値以上である前記属性の値を前記推薦条件に追加する前記推薦条件修正処理を、前記コンピュータに実行させる、請求項１４または１５の推薦条件修正プログラム。 The recommended condition correction processing for adding the value of the attribute having the first contribution rate equal to or higher than the first additional threshold and the second contribution rate equal to or higher than the second additional threshold to the recommended condition. The recommended condition correction program according to claim 14 or 15, wherein the program is executed.
  17. 　前記コンテキストは、前記属性の一つとしてユーザ識別子を包含し、
    　少なくとも一部をユーザ端末から受信した、ユーザの前記コンテキストが前記推薦条件を満たす場合に、前記推薦条件を満たすと判断された前記コンテキストと前記ユーザが前記所定の行動をとっていないことを示す前記成否情報を前記推薦履歴として前記推薦履歴記憶手段に格納する検索処理と、
    　成功履歴入力端末から、成功ユーザの前記コンテキストを取得して、前記推薦履歴記憶部に、取得された当該コンテキストが含む前記ユーザ識別子と一致する前記ユーザ識別子を含む前記推薦履歴が存在するかを検索し、
    存在すれば、検索された前記推薦履歴の前記成否情報を前記対象ユーザが前記所定の行動をとったことを示すように更新し、存在しなければ、前記成功履歴入力端末から取得された前記コンテキストを前記成功履歴記憶部に格納する成功履歴登録処理を、さらに前記コンピュータに実行させる、請求項１３乃至１６のいずれかに記載の推薦条件修正プログラム。     The context includes a user identifier as one of the attributes,
    When the context of the user who has received at least a part from the user terminal satisfies the recommendation condition, the context determined to satisfy the recommendation condition and the user does not take the predetermined action Search processing for storing success / failure information as the recommendation history in the recommendation history storage means;
    Obtain the context of a successful user from a success history input terminal, and search in the recommendation history storage unit whether the recommendation history including the user identifier that matches the user identifier included in the acquired context exists. And
    If it exists, the success / failure information of the retrieved recommendation history is updated to indicate that the target user has taken the predetermined action, and if not, the context acquired from the success history input terminal The recommendation condition correction program according to any one of claims 13 to 16, further causing the computer to execute a success history registration process for storing the success history in the success history storage unit.
  18. 　前記推薦条件に加え、ユーザが前記所定の行動をとったことを判別するために前記コンテキストから選択された、１以上の前記属性の値が満たすべき条件を含む期待条件を前記条件記憶手段に格納する前記条件記憶処理と、
    　前記コンテキストは、前記属性の一つとしてユーザ識別子を包含し、
    　少なくとも一部を前記ユーザ端末から取得した、ユーザの前記コンテキストが前記推薦条件を満たす場合に、取得された前記コンテキストと前記ユーザが前記所定の行動をとっていないことを示す前記成否情報を前記推薦履歴として前記推薦履歴記憶手段に格納する検索処理と、
    　少なくとも一部を前記ユーザ端末から取得した、前記ユーザの前記コンテキストが前記期待条件を満たす場合に、取得された当該コンテキストが含む前記ユーザ識別子と一致する前記ユーザ識別子を含む前記推薦履歴が存在するかを検索し、存在すれば、検索された前記推薦履歴の前記成否情報を前記対象ユーザが前記所定の行動をとったことを示すように更新し、存在しなければ、取得された前記コンテキストを前記成功履歴記憶部に格納する成功履歴登録処理を、さらに前記コンピュータに実行させる、請求項１３乃至１６のいずれかに記載の推薦条件修正プログラム。     In addition to the recommended condition, an expected condition including a condition to be satisfied by one or more values of the attribute selected from the context in order to determine that the user has taken the predetermined action is stored in the condition storage unit The condition storage processing to
    The context includes a user identifier as one of the attributes,
    When the user's context that has acquired at least a part from the user terminal satisfies the recommendation condition, the acquired context and the success / failure information indicating that the user has not taken the predetermined action are recommended. A search process to be stored in the recommendation history storage means as a history;
    Whether or not the recommendation history including the user identifier that matches the user identifier included in the acquired context exists when the context of the user acquired at least a part from the user terminal satisfies the expected condition If it exists, the success / failure information of the searched recommendation history is updated so as to indicate that the target user has taken the predetermined action. The recommended condition correction program according to any one of claims 13 to 16, further causing the computer to execute a success history registration process stored in a success history storage unit.
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