WO2016075826A1 - Système de recommandation, procédé de recommandation et programme de recommandation - Google Patents

Système de recommandation, procédé de recommandation et programme de recommandation Download PDF

Info

Publication number
WO2016075826A1
WO2016075826A1 PCT/JP2014/080258 JP2014080258W WO2016075826A1 WO 2016075826 A1 WO2016075826 A1 WO 2016075826A1 JP 2014080258 W JP2014080258 W JP 2014080258W WO 2016075826 A1 WO2016075826 A1 WO 2016075826A1
Authority
WO
WIPO (PCT)
Prior art keywords
plan
user
course
plan information
date
Prior art date
Application number
PCT/JP2014/080258
Other languages
English (en)
Japanese (ja)
Inventor
ロビン スウィジー
Original Assignee
楽天株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 楽天株式会社 filed Critical 楽天株式会社
Priority to JP2016558840A priority Critical patent/JP6133518B2/ja
Priority to PCT/JP2014/080258 priority patent/WO2016075826A1/fr
Publication of WO2016075826A1 publication Critical patent/WO2016075826A1/fr

Links

Images

Classifications

    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services

Definitions

  • One aspect of the present invention relates to a recommendation system, a recommendation method, and a recommendation program.
  • Information systems that sell products etc. via the Internet are known. Some of such information systems have a recommendation function based on collaborative filtering. That is, the usage history of the product purchased by the user, the product viewed, or the like is stored, and based on the usage history, a product or the like that matches the user's preference or tendency is extracted and presented to the user.
  • an information system that reserves and settles a plan for golf, travel, etc. via the Internet is known (see, for example, Patent Document 1).
  • the golf plan includes, for example, course information regarding the location of the golf course and date and time information.
  • the plan includes information such as fees and other conditions.
  • the presented plan may not be useful to the user. For example, even if a plan similar in location, fee, etc. to a plan used in the past is presented based on the user's usage history, if the plan is not for a schedule that can be used by the user, the recommendation is useful to the user Is low. In addition, it is difficult to predict a schedule for a user to use the plan based on the user's usage history.
  • an object of one aspect of the present invention is to provide a system capable of presenting a plan useful to the user to the user.
  • a recommendation system is a recommendation system that recommends a plan associated with a date to a user, and at least one of purchase and browsing of the user's plan Extraction means for extracting a plurality of plan information based on the history relating to one, and display control means for displaying the plan information extracted by the extraction means for each date in a time series in the first display field.
  • a recommendation method is a recommendation method in a recommendation system that recommends a plan associated with a date to a user, and is based on a history of at least one of purchase and browsing of the user's plan.
  • there is an extraction step for extracting a plurality of plan information and a display control step for displaying the plan information extracted in the extraction step for each date in time series in the first display field.
  • a recommendation program is a recommendation program for causing a computer to function as a recommendation system that recommends a plan associated with a date to a user, and allows the computer to purchase and view the user's plan.
  • An extraction function for extracting a plurality of plan information based on a history relating to at least one of them, and a display control function for displaying the plan information extracted by the extraction function for each date in time series in the first display column And realize.
  • the plan extracted based on the user's history is displayed for each date, the plan is presented to the user for each date.
  • the user can browse a plan associated with an available date. Therefore, the user can obtain useful plan information.
  • the display control unit displays, in association with the second date, plan information including an attribute different from the attribute associated with the plan information displayed in association with the first date. May be.
  • the user since the plan information having an attribute different from the plan information displayed in association with the first date is displayed in association with the second date, the user obtains various plan information. Can do.
  • the recommendation system further includes a receiving unit that receives a selection input from the user for the displayed plan information, and the display control unit is associated with the same date as the plan information selected by the user. May be displayed in a second display field different from the first display field.
  • the plan information associated with the same date as the plan information selected by the user is presented to the user.
  • the user can obtain a plurality of pieces of plan information associated with dates that can be used by the user.
  • the plan information is associated with an attribute including at least a place in addition to the date
  • the accepting unit receives the plan information displayed in the second display field from the user.
  • the display control means displays the plan information selected by the user in the second display field and the plan information associated with the attribute of the same location for each date in the first display field. It is good.
  • plan information having the same location attribute as the plan information selected by the user and associated with another date is presented to the user together with the selected plan information.
  • the user can obtain plan information regarding a place that meets his / her wishes for more dates.
  • the frequency of purchasing or browsing a plan for one or more specific days is more than a predetermined level than the frequency of purchasing or browsing a plan for a day other than a specific day of the week.
  • the display control means may display only the plan for a specific day of the week for each date in the first display field.
  • the plan information associated with the day of the week whose frequency is high is presented.
  • the user can obtain the plan information associated with the date with high possibility that the confidence can be used.
  • the extraction unit may extract the plan information based on the score calculated based on the user history.
  • plan information that is highly likely to meet the user's wishes is extracted based on the user's history and presented to the user, so that the user can obtain useful plan information.
  • the plan information is plan information relating to golf play, and includes information on a golf course as an attribute relating to a place, and information relating to a date and time of play as an attribute relating to a date. It is good.
  • the user can obtain plan information regarding golf play.
  • FIG. 1 is a block diagram showing a functional configuration of a recommendation system 1 according to the present embodiment.
  • the recommendation system 1 of this embodiment is a system that recommends a plan to a user.
  • the plan may be a target of browsing and purchase by the user via the Internet, and at least information related to the date may be associated, and further information related to the location may be associated.
  • the plan is, for example, a golf plan or a travel plan.
  • the golf plan is associated with the date of play as information relating to the date, and the information relating to the golf course is associated as information relating to the place.
  • the travel schedule is associated with information related to the date, and the information related to the destination of the travel is correlated as information related to the place. This embodiment will be described with an example of a golf plan.
  • the golf plan includes, for example, course information and date / time information regarding the location of the golf course.
  • the plan includes information such as fees and other conditions.
  • a conventional information system that reserves and settles a golf plan via the Internet, for example, accepts an input specifying a golf course from a user and presents a plan associated with the designated golf course.
  • some information systems that sell products and the like via the Internet make recommendations by collaborative filtering.
  • usage histories of products purchased or browsed by the user are stored, products based on the user's preferences and trends are extracted based on the usage history, and presented to the user To do.
  • a recommendation by collaborative filtering can be applied. For example, a golf course that another user who has been to a golf course that the user has been to has performed can be presented to the user.
  • the recommendation system 1 solves such a problem and enables the user to present a plan that meets the user's wishes.
  • the recommendation system 1 of the present embodiment includes a recommendation device 10 and a terminal 30.
  • the terminal 30 is a user terminal, and can communicate with the recommendation device 10 via a network N such as the Internet.
  • the recommendation device 10 can access the plan information storage unit 21 and the user information storage unit 22.
  • the recommendation device 10 functionally includes an acquisition unit 11, a calculation unit 12 (extraction means), and a transmission unit 13 (extraction means).
  • the terminal 30 includes a user ID transmission unit 31, a plan information acquisition unit 32 (extraction unit), a display control unit 33 (display control unit), and a reception unit 34 (reception unit).
  • the plan information storage unit 21 is a storage unit that stores plan information.
  • the plan information storage unit 21 may be configured as one functional unit of the recommendation device 10.
  • the plan information storage unit 21 may be configured as a computer device that can communicate with the recommendation device 10 via a network. Details of the plan information will be described later.
  • the user information storage unit 22 is a storage unit that stores a user's plan purchase history as user information.
  • the user information storage unit 22 may be configured as one function unit of the recommendation device 10.
  • the user information storage unit 22 may be configured as a computer device that can communicate with the recommendation device 10 via a network. Details of the plan information will be described later. Details of the purchase history of the plan as user information will be described later.
  • FIG. 2 is a hardware configuration diagram of the recommendation device 10.
  • the recommendation device 10 is physically composed of a CPU 101 constituted by a processor, a main storage device 102 constituted by a memory such as a RAM and a ROM, an auxiliary storage device 103 constituted by a hard disk, etc., a network
  • the computer system includes a communication control device 104 including a card, an input device 105 such as a keyboard and mouse as input devices, an output device 106 such as a display, and the like.
  • the recommendation device 10 may not include the input device 105 and the output device 106.
  • Each function of the recommendation device 10 shown in FIG. 1 is executed under the control of the CPU 101 by causing a predetermined computer software (recommendation program) to be read on hardware such as the CPU 101 and the main storage device 102 shown in FIG. This is realized by operating the communication control device 104, the input device 105, and the output device 106, and reading and writing data in the main storage device 102 and the auxiliary storage device 103. Data and databases necessary for processing are stored in the main storage device 102 and the auxiliary storage device 103.
  • the terminal 30 is also a computer system having the same hardware configuration as that of the recommendation device 10.
  • the terminal 30 is configured as a device such as a personal computer or a smartphone, for example.
  • the acquisition unit 11 is a part that acquires a user ID that identifies a user. Specifically, the acquisition unit 11 acquires the user ID transmitted from the terminal 30.
  • the calculation unit 12 is a part that calculates a score for each plan based on the plan selection history by the user specified by the user ID.
  • the user selects a plan for purchasing or viewing the plan.
  • the plan selection history is a history regarding at least one of plan purchase and browsing. Details of the score calculation will be described later.
  • the calculation unit 12 calculates a score with reference to a golf plan purchase history. Since the plan browsing history includes plan information in the same manner as the purchase history, the score for each plan may be calculated based on the browsing history, similar to the calculation of the score based on the purchase history.
  • FIG. 3 is a diagram illustrating an example of purchase history data of a user's golf plan.
  • the user information storage unit 22 stores the purchase history of the user's golf plan, and the calculation unit 12 can acquire the purchase history of the user based on the user ID by accessing the user information storage unit 22.
  • the user information storage unit 22 stores, for example, a plan ID, a play date, a course ID, a plan fee, and the like as a user purchase history for each user ID.
  • the plan ID is identification information for identifying a golf plan.
  • the play date is information on the date on which the golf plan was played.
  • the course ID is identification information for identifying the golf course according to the golf plan.
  • the plan fee is a fee for the golf plan.
  • FIG. 4 is a diagram illustrating an example of the configuration of the items of plan information stored in the plan information storage unit 21.
  • the plan information includes various types of information indicating the contents of the plan in association with the plan ID.
  • the course ID is information for identifying a golf course.
  • the plan name is the name of the plan.
  • the plan fee is a fee for purchasing the plan.
  • the public period start and public period end are information indicating the start date and the end date of the period in which the plan is implemented.
  • the day of the week classification and the target day of the week are information indicating the day of the week to which the plan is applied.
  • the target start time zone is information indicating a time zone in which play of the plan can be started. That is, the start of the public period, the end of the public period, the day of the week classification, the target day of the week, and the target start time zone correspond to data indicating attributes regarding the date of the plan.
  • the plan information further includes information on other options.
  • Lunch is information indicating whether lunch is included in the plan.
  • the caddy flag is information indicating whether a caddy is included in the plan.
  • the cart code is information indicating the type of cart used when playing.
  • the number of players (minimum) and the number of players (maximum) are information indicating the lower limit and the upper limit of a set of players when playing with the plan.
  • the plan information includes information on 4-sum discount, 2-sum guarantee, option 1 and option 2, and the like.
  • the calculation unit 12 can acquire the content of the golf plan purchased by the user by referring to the plan information using the plan ID included in the purchase history of the user as a key. And the calculation part 12 can produce
  • FIG. 5 is a diagram illustrating an example of items and values of user behavior data.
  • the user behavior data is data obtained by converting a user purchase history into a format suitable for information processing in order to perform information processing related to the user purchase history.
  • the user behavior data may be generated as multidimensional vector data including values of various items, for example. Specifically, the user behavior data includes values of various items included in the purchase history of the user, and includes an average value and a standard deviation, or a value obtained by normalizing them.
  • MAX, COUNT, AVG, and STD indicate the maximum value, the count value, the average value, and the standard deviation of the values in parentheses, respectively.
  • MAX (play_date) indicates the most recent play date.
  • COUNT (c_id) indicates the number of course IDs included in the purchase history
  • COUNT (DISTINCT c_id) indicates the number of course IDs included in the purchase history excluding duplicates.
  • HOUR (start_time) indicates the start time of play.
  • "lunch_flg” indicates a flag with lunch.
  • caddie_flag indicates a flag with caddy.
  • play_fee indicates a plan fee.
  • “play_mem” indicates the number of players to play.
  • play_day_weakend_flg is a flag indicating a weekend in the plan information.
  • play_day_hol_flg is a flag indicating a holiday in the plan information.
  • golfCourceLat indicates the latitude of the location of the golf course.
  • golfCourceLon indicates the longitude of the location of the golf course.
  • the generation of user behavior data based on the purchase history of the user can be realized by a well-known technique.
  • the user behavior data may be generated by the calculation unit 12, or may be generated by another functional unit or another device.
  • the example of the user behavior data shown in FIG. 5 represents the purchase history of the user, but it is possible to generate data that represents the contents of one plan in the same format as the user behavior data. That is, the data representing the contents of the plan can be represented, for example, as vector data including a plurality of items indicating the contents of the plan and the values of the items. Such data can also be realized by a known technique.
  • Such data facilitates various information processing related to the contents of the plan. That is, the user behavior data can also be said to indicate the features of the plan that the user prefers. Therefore, by comparing the data indicating the contents of the plan with the user behavior data, calculating the distance between the two vector data, etc. The similarity between the plan and the plan preferred by the user can be determined. Moreover, the similarity between users at the time of collaborative filtering can be determined by comparison with the user behavior data of other users or by calculating the distance between vector data.
  • the transmission unit 13 is a part that transmits the plan information whose score is calculated by the calculation unit 12 to the terminal 30 together with the calculated score.
  • the transmission unit 13 may transmit a predetermined number of plan information set in advance to the terminal 30.
  • the user ID transmission unit 31 transmits the user ID to the recommendation device 10.
  • the user ID transmission unit 31 transmits the user ID input in the login process in the terminal 30 to the recommendation device 10.
  • the plan information acquisition unit 32 is a part that acquires the plan information transmitted from the recommendation device 10. Specifically, the plan information acquisition unit 32 acquires plan information associated with each score from the transmission unit 13 of the recommendation device 10.
  • the display control unit 33 is a part that displays the plan information acquired by the plan information acquisition unit 32 in the first display column displayed on the display unit of the terminal 30 for each date in time series. Details of the display control of the plan information by the display control unit 33 will be described later.
  • the reception unit 34 is a part that receives a selection input from the user for the plan information displayed by the display control unit 33. Processing related to acceptance of selection input for plan information will be described in detail later.
  • FIG. 6 is a flowchart showing a recommendation process in the recommendation system 1 of the present embodiment.
  • the user ID transmission unit 31 of the terminal 30 transmits the user ID to the recommendation device 10 (S1). Subsequently, the acquisition unit 11 of the recommendation device 10 acquires the user ID transmitted in step S1 (S2).
  • FIG. 7 is a flowchart illustrating a first example of the score calculation process in step S3. The score calculation process will be described with reference to FIG.
  • the calculation unit 12 refers to the user information storage unit 22 based on the user ID acquired in step S2, and acquires the purchase history of the user (S11).
  • the calculation unit 12 also refers to the plan information storage unit 21 in order to acquire plan information of the plan included in the purchase history.
  • the calculation unit 12 acquires a plan ID of one plan information purchased in the past as a reference plan (reference plan information) in the purchase history (S12).
  • the calculation unit 12 may acquire the latest plan information in the purchase history as a reference plan.
  • plan information having a high probability of having an attribute that meets the user's wish is acquired as a reference plan.
  • the calculation unit 12 may acquire plan information of a plan purchased at a time corresponding to the current season as a reference plan.
  • the calculation unit 12 acquires a course ID (reference location information) associated with the plan ID of the plan acquired as the reference plan (S13). And the calculation part 12 acquires course ID (location information candidate) of the course to recommend based on course ID of a reference plan (S14).
  • the calculation unit 12 determines at least one of a plurality of methods for acquiring the course ID based on the purchase history of the user, and acquires the course ID using the determined method. Since the plan is extracted based on the acquired course ID, determining the method for acquiring the course ID means determining the method for extracting the plan.
  • Examples of the method for acquiring the course ID include a method based on the purchase history of the user, a method based on collaborative filtering, a method based on the golf course and the geographical information of the user, and the like.
  • the course ID acquisition process for acquiring this course ID (location information) will be described below with reference to FIG.
  • FIG. 8 is a flowchart showing an example of course ID acquisition processing in step S14.
  • the calculation unit 12 acquires a course ID of a course that satisfies a predetermined condition among courses associated with a plan included in the purchase history of the user as information on a recommended course (S21).
  • the predetermined condition is, for example, that the course has been used a predetermined number of times or more in the user purchase history. Thereby, since the course ID of the recommended course is acquired by using the user history preferentially, the user history is more strongly reflected in the acquired course ID.
  • the course ID of the course is preferentially used.
  • the predetermined number of times as a condition for acquiring the course ID may be one.
  • the predetermined number of times as a condition for acquiring the course ID may be two or more. In this way, only the course ID of a course purchased multiple times is acquired.
  • the calculation unit 12 determines whether or not the number of course IDs acquired in step S21 is less than a predetermined number set in advance (S22). If it is determined that the number of course IDs is less than the predetermined number, the process proceeds to step S23. On the other hand, when it is not determined that the number of course IDs is less than the predetermined number, the course ID acquisition process ends.
  • step S23 the calculation unit 12 obtains a course ID based on at least one of collaborative filtering based on the user's history and geographical information associated with the user (S23).
  • the calculation unit 12 refers to, for example, the purchase history of the user's golf plan (see FIG. 3) and associates the same course with the course associated with the plan that the user has purchased. Extract other users who have purchased the same plan. And the calculation part 12 acquires course ID of the course matched with the plan in the purchase history of another user. Further, the calculation unit 12 calculates the degree of similarity between the user and another user using the user behavior data as described with reference to FIG. The course ID of the course associated with the plan in the purchase history may be acquired.
  • the calculation unit 12 acquires the course ID of a course that is geographically close to the reference plan course (for example, located within a predetermined distance). . Further, the calculation unit 12 may acquire a course ID of a course that is geographically close to the user's location (for example, located within a predetermined distance).
  • step S23 is repeated until the number of acquired course IDs exceeds a predetermined number.
  • the calculation unit 12 determines any one of a plurality of methods as a method for acquiring the course ID, and performs the course ID acquisition process.
  • the predetermined number of times in the predetermined condition of step S21 is 2 and the predetermined number in step S22 is 40, it is used twice or more in the user's history out of 40 course IDs. All the course IDs of the course are extracted, and other course IDs extracted by collaborative filtering or geographical information are included. That is, the more courses that are used more than once, the greater the proportion of course IDs extracted based on the history out of 40 course IDs. If it does in this way, it can control to adjust the ratio of the method for extracting course ID according to a user's use situation.
  • the calculation unit 12 may perform course ID acquisition processing according to the number of purchased plans in the user purchase history, instead of the processing of steps S21 to S23. That is, when the number of purchased plans is greater than or equal to a predetermined number in the purchase history of the user, the calculation unit 12 determines a predetermined condition (step S21) among courses associated with the plan included in the purchase history. If the number of purchased plans is less than the predetermined number in the user's purchase history, the course ID of the course satisfying the same condition as in the predetermined condition is acquired based on the user's history. A course ID is acquired based on at least one of collaborative filtering and geographical information associated with the user.
  • the calculation unit 12 performs course ID acquisition processing using any one of a method based on the purchase history of the user, a method based on collaborative filtering, a method based on the golf course and the geographical information of the user, and the like. carry out.
  • the purchase history may not properly reflect the user's wishes.
  • the course ID is acquired based on the location information associated with the plan included in the purchase history of the user, and thus the user's wish is appropriately reflected in the acquired course ID. It will be.
  • the course ID is acquired based on collaborative filtering or the user's geographical information. can get.
  • the calculation unit 12 acquires a course ID based on the degree of matching between the plan extracted based on the course ID acquired using each of the plurality of methods and the plan included in the purchase history of the user. A technique for doing so may be determined. That is, the calculation unit 12 acquires a course ID by each of a plurality of predetermined methods, acquires a plan information candidate associated with the course ID for each predetermined method, calculates a score of each plan information candidate, And plan extraction from plan information candidates based on the calculated score, and a plan that recommends a predetermined method with the highest degree of matching of the extracted plan to the plan included in the history to the user It is good also as determining as a method for extracting. The calculation of the score for each plan and the extraction of the plan based on the score will be described later.
  • the calculation unit 12 is based on, for example, a method based on the purchase history of the user, a method based on collaborative filtering (may be a plurality of types of collaborative filtering with different parameters and weights), a golf course, and geographical information of the user.
  • a course ID is acquired by each method.
  • the calculation unit 12 acquires plan information associated with the course ID acquired by each method as a plan information candidate, calculates the score of the acquired plan information candidate, and becomes a candidate for a plan to be recommended. Extract plans based on score.
  • the calculation unit 12 determines the degree of matching between the plan extracted for each method and the plan included in the purchase history of the user, and uses the method with the highest degree of matching as a method for acquiring the course ID. decide.
  • the optimum method for acquiring the location information candidate is selected.
  • a method having a high probability of extracting a plan that meets the user's wish is determined as a method for extracting a plan recommended to the user.
  • the probability that the plan which suits a user's hope is extracted is improved.
  • a method for acquiring the course ID may be determined as follows.
  • the calculation unit 12 refers to the purchase history of the user and acquires the number of courses having a purchase history of a plurality of times (for example, twice) among golf courses associated with the purchased plan. . Then, the calculation unit 12 calculates the ratio of the number of courses having a history of multiple purchases to the total number of courses that can be selected in the recommendation system 1, and when the calculated ratio is equal to or greater than a predetermined value, A process based on the purchase history is preferentially used to execute a process for acquiring the course ID.
  • the process preferentially using the method based on the purchase history of the user may be selecting a method based on the purchase history of the user as the process of acquiring the course ID, or in the case of using a plurality of methods, The weighting of the method based on the purchase history of the user may be made heavier than other methods.
  • the meaning of the process that preferentially uses one method is the same as that described above in the description of the determination of the method for acquiring the following course ID.
  • the calculation unit 12 refers to the purchase history or browsing history of the user, and uses a certain method among a plurality of methods with respect to the total number of plans for which selection input has been performed for purchase or browsing. Calculate the percentage of the number of plans that were selected and entered for purchase or viewing with respect to the recommended plan, and if the calculated percentage is greater than or equal to a predetermined value, use the method preferentially, It is good also as implementing the process which acquires course ID.
  • the calculation unit 12 acquires the page currently displayed on the user terminal 30. If the displayed page is a search screen for searching for a plan, the calculation unit 12 has a high probability that the user desires a plan or course that is not included in his / her history.
  • the course ID may be acquired by preferentially using a method other than the method based on the purchase history of the user.
  • the calculation unit 12 may preferentially use a method based on the purchase history of the user to perform the process of acquiring the course ID.
  • the calculation unit 12 acquires whether the current time corresponds to a so-called busy period, a non-busy period, or a quiet period based on the current date. Correspondence between each date and a busy period, a non-busy period, and a quiet period is set in advance. In view of the fact that the user tends to use a course that the user has used in the past during the busy season, the calculation unit 12 uses a method based on the purchase history of the user when the current time is the busy season. It is good also as carrying out the process which acquires and uses course ID preferentially.
  • the calculation unit 12 A process other than the technique based on the purchase history of the user may be preferentially used to perform the process of acquiring the course ID.
  • the calculation unit 12 acquires the plan ID associated with the course ID acquired in step S14 and the plan information as plan information candidates to be recommended to the user (S15). ). Specifically, the calculation unit 12 refers to the plan information storage unit 21, and among the plan IDs associated with the course ID acquired in step S14, for example, from the current date as a playable day. A plan ID and plan information associated with dates from one week to three weeks later are acquired.
  • the calculation unit 12 calculates a score for each plan information candidate acquired in step S15 (S16). Specifically, the calculation unit 12 calculates a score based on at least one of the purchase history of the user and the plan information of the reference plan and the attribute of the plan information candidate.
  • the calculation unit 12 calculates the score of the plan information candidate based on the similarity between the attribute of the plan information candidate and the purchase history or the attribute of the reference plan.
  • the score calculated in this way has a higher value as the similarity is higher. This will be specifically described below.
  • the calculation unit 12 calculates a score for each plan information candidate by the following equation (1).
  • the left side in Expression (1) represents the score of the plan information candidate.
  • the first term on the right side represents the similarity of charges between the plan information candidate and the reference plan or user behavior data of the user.
  • w p is a predetermined coefficient and is set by design.
  • the details of the first term on the right side can be expressed, for example, as in Expression (2).
  • price ref , u) on the first side in Expression (2) represents the similarity between the plan information candidate charge and the charge of the reference plan or the user behavior data of the user.
  • t and t ref mean that the similarity of time is taken into account in the calculation of the similarity.
  • the second term in the denominator of the second side of Equation (2) is the distance between the price of the plan information candidate and the price of the reference plan or the user behavior data of the user.
  • SD price user
  • AVG price t
  • AVG price tref
  • the weekend ratio is a coefficient for correcting the difference between the weekday charge and the weekend charge (Saturday, Sunday, public holiday).
  • the plan information candidate plans are for weekends.
  • the plan information candidate plan is for a weekday, and the reference plan is for a weekend. If it is, a number smaller than 1 (for example, the reciprocal of 1.4) is set.
  • This coefficient is set empirically or empirically. Also, if both the plan information candidate plan and the reference plan are for weekdays, or if both the plan information candidate plan and the reference plan are for weekends, this coefficient is set to 1. Is done. “price” and “price ref” are charges for the plan information candidate and the reference plan, respectively.
  • the second term on the right side of Expression (1) represents an optional similarity between the plan information candidate and the reference plan or user behavior data of the user.
  • the option is the presence / absence of lunch, caddy, discount, etc. in the contents of the golf plan, and the opt is data of these presence / absence (for example, as vector data).
  • w k is a predetermined coefficient and is set by design.
  • n is the number of optional items.
  • the third term on the right side of Expression (1) represents the similarity of the attribute regarding the course between the plan information candidate and the reference plan or the user behavior data of the user.
  • the course is data (for example, as vector data) of attributes related to the course of the plan information candidate.
  • the course ref is obtained by converting the attribute regarding the course in the reference plan or the user behavior data of the user into data (for example, as vector data).
  • the plan information candidate is obtained by converting the plan information candidate and the attribute of the reference plan or the user behavior data of the user into data (for example, as vector data) and calculating the similarity (for example, the distance between the vectors).
  • the score is calculated.
  • the similarity for each element such as fee, various attributes of the plan, course, etc. is calculated, and the weight is adjusted by the coefficient.
  • the score is calculated by adding the similarities. By calculating the score in this way, various elements are appropriately reflected in the score. Therefore, the plan recommendation based on the score is highly likely to meet the user's wishes.
  • the score calculation according to the expressions (1) and (2) is an example of the score calculation, and the similarity used for the score calculation can be calculated by various known techniques. By calculating the score in this way, the calculated score reflects the degree of plan information candidate user's desire.
  • the plan includes various elements such as fees, various attributes of the plan, and courses, but the conventional recommendation system recommends a golf course that is another course by focusing on the course that is one element. It was to stay in.
  • collaborative filtering may not function effectively when the effective period such as a plan is limited.
  • collaborative filtering by courses has been performed focusing on courses that exist for a long time and are considered to be important elements for users.
  • the course is only one element of the plan.
  • the user selects the plan by paying attention to the elements of the plan other than the course, or selects the plan by comprehensively judging a plurality of elements. It is thought that there is.
  • the plan is decomposed into elements, the similarity between the reference plan and the plan information candidate is calculated for each element, and the overall score of the plan is calculated. . With such a score, it is possible to extract and recommend a plan that suits the user's preference while flexibly considering various factors.
  • the calculation unit 12 refers to the user information storage unit 22 based on the user ID acquired in step S2, and acquires a user purchase history (S31).
  • the calculation unit 12 also refers to the plan information storage unit 21 in order to acquire plan information of the plan included in the purchase history.
  • the calculation unit 12 determines whether or not there is a predetermined tendency with respect to the location associated with the plan, that is, the location where the golf course is located (S32).
  • the predetermined tendency is, for example, a tendency that the user selects a course and a plan according to the distance to the golf course.
  • the calculation unit 12 determines a predetermined location-related information when there is a correlation of a predetermined level or more between the distance from the user's location to the course and the start time of the play. It is determined that there is a tendency. The presence or absence of correlation can be determined by a well-known statistical process.
  • the calculation unit 12 determines a predetermined place regarding the place when the ratio of the course at a certain distance from the user's location is equal to or greater than a predetermined degree. It may be determined that there is a tendency. Further, for example, the calculation unit 12 may determine that there is a predetermined tendency regarding the location when the standard deviation of the geographical parameter in the user behavior data of the user is equal to or less than a predetermined value.
  • the calculation unit 12 calculates a score for each piece of plan information according to the presence or absence of a predetermined tendency regarding the place determined in step S32. (S33).
  • the calculation of the score may be performed using Expression (1) and Expression (2) similarly to Step S16.
  • the calculation unit 12 sets weights for geographical parameters according to the presence or absence of a predetermined tendency regarding the place.
  • the geographical parameter is, for example, a parameter related to the location among various parameters used for calculating the score, and is a parameter related to the location of the course, the location of the user, and the like.
  • the calculation unit 12 uses a geographic parameter in calculating the score, compared with a case where it is not determined that there is a predetermined tendency. In some cases, the weighting of these parameters is increased. By calculating the score in this way, the user's desire for the plan is more appropriately reflected on the calculated score.
  • the calculation unit 12 refers to the user information storage unit 22 based on the user ID acquired in step S2, and acquires the purchase history of the user (S41).
  • the calculation unit 12 also refers to the plan information storage unit 21 in order to acquire plan information of the plan included in the purchase history.
  • the calculation unit 12 acquires, as a reference plan (reference plan information), the plan ID of one plan information purchased in the past in the purchase history (S42).
  • the process of step S42 is the same as the process of step S12.
  • the calculation unit 12 determines whether or not there is a predetermined tendency with respect to the location associated with the plan, that is, the location where the golf course is located, in the plurality of plans included in the purchase history of the user (S43).
  • the process of step S43 is the same as the process of step S32.
  • step S44 acquires a course ID (reference location information) associated with the plan ID of the plan acquired as the reference plan (S44).
  • the process of step S44 is the same as the process of step S13.
  • the calculation unit 12 acquires a course ID (location information candidate) of a recommended course based on the course ID of the reference plan (S45).
  • the calculation unit 12 performs course ID acquisition processing by weighting the geographical parameters. Specifically, for example, in the process of step S14 described with reference to FIG. 8, after the course ID is acquired based on the purchase history of the user (S21), the acquired course ID is less than a predetermined number.
  • the calculation unit 12 first acquires the course ID based on the geographical information, and then the number of acquired course IDs reaches a predetermined number. When there is no course ID, the course ID may be acquired by the user's purchase history or collaborative filtering.
  • step S46 acquires the plan ID associated with the course ID acquired in step S45 and the plan information as plan information candidates to be recommended to the user (S46).
  • the process of step S46 is the same as the process of step S15.
  • step S47 calculates a score for each piece of plan information according to the presence or absence of the predetermined tendency related to the place determined in step S43.
  • the process of step S47 is the same as the process of step S33.
  • the calculation unit 12 increases the weighting of the geographical parameter in calculating the score.
  • the calculation unit 12 is fixed from the user's location. In order to increase the score of the course within the distance, the weight of the course location is increased when calculating the score. In addition, the weight of the course location may be increased when calculating the score so that the score of the course in which the required time from the user location to the course location is within a certain time is high. On the other hand, for a course that is not within a certain distance from the user's location, the calculation unit 12 is similar to a plan with a late play start time or a course attribute other than the location in the purchase history of the user. Increase the score of such a plan so that the plan is recommended.
  • the calculation unit 12 calculates the score by weighting the course location and the start time of the play so as to increase the score of such a plan.
  • the calculation unit 12 makes a recommendation so that the plan for the course in the area is recommended. , Increase the weight of the course location.
  • the transmission unit 13 of the recommendation device 10 transmits plan information including the score calculated in step S3 to the terminal 30 (S4).
  • the calculation unit 12 may extract plan information based on the calculated score, and cause the transmission unit 13 to transmit the extracted plan information.
  • the calculation unit 12 may extract a predetermined number of plans having a high calculated score from the top ones and cause the transmission unit 13 to transmit the plan information of the extracted plans.
  • the plan information acquisition unit 32 of the terminal 30 acquires the plan information transmitted from the recommendation device 10 (S5).
  • the plan information transmitted to the terminal 30 includes at least a play date, course (course ID) information (location information), and a score. All may be included.
  • the display control unit 33 of the terminal 30 displays the plan information acquired in step S5 on the display unit of the terminal 30 (S6).
  • the display control unit 33 may extract a predetermined number of plan information from the acquired plan information in descending order of score, and display the extracted plan information on the display unit.
  • the display control unit 33 may display the plan information acquired by the plan information acquisition unit 32 on a display column displayed on the display unit of the terminal 30 for each date in time series. An example of this display processing will be described with reference to FIG.
  • the display control unit 33 extracts plan information for each date from the plan information received in step S5 (S51).
  • the display control unit 33 displays the plan information extracted for each date in the first display column for each date in time series.
  • the first display field is configured in a calendar format, for example.
  • the calendar represents dates in a tabular format.
  • FIG. 12 is a diagram illustrating a display example of the plan information displayed in step S52. As shown in FIG. 12, the plan information is displayed for each calendar date.
  • the display control unit 33 associates the plan information with the highest score among the plurality of pieces of plan information associated with the same date with the date.
  • the plan information selected at random from a plurality of pieces of plan information associated with the same date may be displayed in association with the date.
  • FIG. 13 is a diagram illustrating an example of plan information display control. Specifically, in FIG. 13, the display control unit 33 displays the plan information of the course “AAA country” in association with the date “January 14” (first date). At this time, the display control unit 33 displays the plan information of the course “BBB golf club”, which is an attribute relating to a place different from the course “AAA country”, in association with the date “January 15” (second date). Let Thus, since the plan information having an attribute different from the plan information displayed in association with the first date is displayed in association with the second date, the user can obtain various plan information. .
  • step S54 the display control unit 33 displays other plan information associated with the same date as the selected plan in a second display field different from the first display field (S54). Specifically, the display control unit 33 displays other plan information associated with the same date as the selected plan in another field (second display) different from the calendar display field (first display field). Column).
  • FIG. 14 is a diagram showing a display example of the plan information displayed in another column. As shown in FIG. 14, when the accepting unit 34 accepts a selection input for the plan information displayed in the column “January 15” in the calendar column C, the display control unit 33 displays the date “January 15”. Plan information associated with "day” is displayed in another column D.
  • the display control unit 33 is plan information associated with the date “January 15”, and includes courses “AAA country”, “BBB golf club”, “EEE golf club”, “FFF country”. ”And“ GGG country ”are displayed in another column D in association with the five plan information respectively associated with the location attributes.
  • plan information associated with the same date as the plan information selected by the user is presented to the user.
  • the user can obtain a plurality of pieces of plan information associated with dates that can be used by the user.
  • the accepting unit 34 determines whether or not a selection input from the user has been accepted for the plan information displayed in another field D which is the second display field (S55). If a plan selection input is accepted, the process proceeds to step S56.
  • the display control unit 33 displays the plan information selected by the user in another column D and the plan information in which the course that is the attribute of the same location is associated with the calendar column C as the first display column. Display by date.
  • FIG. 15 is a diagram showing a display example of the plan information displayed in the calendar column C.
  • the display control unit 33 displays the course “EEE Golf Club” which is an attribute related to the place.
  • the plan information in which the dates“ January 13 ”to“ January 17 ”displayed in the calendar column C are associated as play date attributes are received from the recommendation device 10. Extracted from the transmitted plan information and displayed in each date column.
  • plan information having the same location attribute as the plan information selected by the user and associated with another date is presented to the user together with the selected plan information. Thereby, the user can obtain plan information regarding a place that meets his / her wishes for more dates.
  • the display control unit 33 may perform display control of plan information based on the purchase history of the user.
  • FIG. 16 is a diagram illustrating a display example of the plan information displayed based on the purchase history of the user. Specifically, the display control unit 33 determines whether or not there is a bias with respect to a specific day of the week for the plan information included in the purchase history of the user. The determination of the presence or absence of bias is realized based on a well-known statistical process. When it is determined that there is a bias with respect to a specific day of the week, the display control unit 33 causes the display unit to display a calendar-type display column that includes only the day of the week that has been determined to have a bias, and displays each display column. The plan information associated with the corresponding date is extracted from the plan information transmitted from the recommendation device 10 and displayed in each date column.
  • the display control unit 33 causes the display unit to display a calendar-type display column composed only of Saturday and Sunday, and displays the dates “January 11” and “January 12” corresponding to the respective display columns. Plan information associated with “day”, “January 18”, “January 19”, “January 25”, and “January 26” is displayed in the respective columns. With such display control, it is possible to present plan information for a schedule that is likely to be purchased by the user. Further, by performing such display control, it is possible to reduce the date to be displayed, so even if the screen space for displaying the plan information to be recommended is limited, the space can be used by the user. Plan information that is highly likely to meet your wishes can be displayed.
  • step S6 ends.
  • FIG. 17A is a diagram showing a recommendation program P10 for causing a computer to function as the recommendation device 10.
  • the recommendation program P10 includes a main module m10, an acquisition module m11, a calculation module m12, and a transmission module m13.
  • the main module m10 is a part that comprehensively controls the recommendation process.
  • the functions realized by executing the acquisition module m11, the calculation module m12, and the transmission module m13 are the same as the functions of the acquisition unit 11, the calculation unit 12, and the transmission unit 13 of the recommendation device 10 illustrated in FIG.
  • FIG. 17B is a diagram showing a terminal recommendation program P30 for causing a computer to function as the terminal 30.
  • the terminal recommendation program P30 includes a main module m30, a user ID transmission module m31, a plan information acquisition module m32, a display control module m33, and a reception module m34.
  • the main module m30 is a part that comprehensively controls the recommendation processing in the terminal 30.
  • the functions realized by executing the user ID transmission module m31, the plan information acquisition module m32, the display control module m33, and the reception module m34 are respectively the user ID transmission unit 31 and the plan information acquisition unit of the terminal 30 shown in FIG. 32, the same functions as those of the display control unit 33 and the reception unit 34.
  • the recommendation program P10 and the terminal recommendation program P30 are provided by storage media D10 and D30 such as a CD-ROM, a DVD-ROM, or a semiconductor memory, for example. Further, the recommendation program P10 and the terminal recommendation program P30 may be provided via a communication network as computer data signals superimposed on a carrier wave.
  • the plan extracted based on the user's history is displayed for each date, so the plan is presented to the user for each date. Is done. Thereby, the user can browse a plan associated with an available date. Therefore, the user can obtain useful plan information.
  • a score for each plan is calculated based on the history of purchase or viewing of the user's plan, and plan information is presented to the user based on the calculated score.
  • the score calculated based on the user's history is highly likely to reflect the degree of user's desire. Since the plan information is presented to the user based on such a score, the user can obtain the plan information of the plan that meets his / her wish.
  • the display control unit 33 is provided in the terminal 30, but the display control unit 33 may be provided in the recommendation device 10.
  • the plan information is displayed on the display unit of the terminal 30 in various manners based on the display control of the display control unit in the recommendation device 10.
  • step S14 of the flowchart of FIG. 7 and step S45 of the flowchart of FIG. 10 as a method for acquiring the course ID, a method based on the purchase history of the user, a method based on collaborative filtering, a golf course, A method based on the geographical information of the user is exemplified.
  • a method for acquiring the course ID the following method may be used.
  • the calculation unit 12 refers to the purchase history of the user as a variation of the method based on the golf course and the geographical information of the user, acquires the position of the golf course associated with the purchased plan, An average position of the acquired positions of the plurality of courses is calculated, and a course ID of a course located at a position close to the calculated average position is acquired.
  • the calculation unit 12 may further acquire the location of the user, and obtain a course ID by weighting the course closer to the user's location as seen from the calculated average position. Further, the calculation unit 12 may perform correction to move the calculated average position by a predetermined distance in the direction of the user's location, and acquire the course ID based on the corrected average position.
  • the calculation unit 12 may use a technique of calculating a score for each course by applying a so-called page rank (registered trademark) algorithm and acquiring a course ID of a course having a high score. For example, a user who has a page rank as a user, a link in the page rank as a participation in a plan purchased by another user, and a user who has purchased a plan in which more users have participated has a high importance and a user with a high importance. It can be considered that the course of the plan selected by is highly important.
  • a user X makes a reservation for a plan associated with course A and three other users (each having a score of 1) participate in the plan, Thus, 3 points, which are the total points of other participating users, are given.
  • the user Y reserves a plan associated with the course B and other users including the user X participate in the plan, the three points that the user X has for the user Y and others The total points of the participating users are given.
  • user Z makes a reservation for a plan associated with course A and another user participates in the plan, similarly, the total of other users' points will be given to user Z. It is done.
  • the calculation unit 12 calculates, as the score of the course A, the total value of the points of the user X, the user Z, and so on who have reserved the plan associated with the course A.
  • the calculation unit 12 may extract and acquire course IDs of a predetermined number of higher-order courses based on the order of scores calculated for each course in this way.
  • the calculation part 12 is good also as acquiring the course ID of the course extracted based on a course and the user's geographical information further with respect to the course acquired in this way.
  • the calculation unit 12 may acquire the course ID by so-called content-based filtering. Specifically, the calculation unit 12 acquires the course ID of the reference plan, for example, by the process shown in step S13 of the flowchart of FIG.
  • the calculation unit 12 refers to the course information that stores the characteristics of each course in association with each other, and has attribute information similar to various attribute information associated with the acquired course ID. Extract the course.
  • the similarity of the course features can be calculated by a known technique. For example, the attribute information for each course is expressed as a vector, and can be calculated as a distance between the vectors. And the calculation part 12 acquires course ID of the acquired other course.
  • the recommendation system 1 is linked to a social networking service (SNS) system and can acquire various information from the SNS system
  • the course ID can also be acquired using the user's network.
  • the calculation unit 12 acquires information on other users related to the user from the SNS system, refers to the purchase history of the other users, and purchases or browses the plans purchased by other users.
  • the course ID of the associated course is acquired as the course ID used for recommending the plan to the user.

Landscapes

  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Finance (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Accounting & Taxation (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Human Resources & Organizations (AREA)
  • Game Theory and Decision Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention concerne un système de recommandation qui recommande à un utilisateur des projets ayant une date associée et qui est pourvu d'un moyen d'extraction qui extrait plusieurs ensembles d'informations de projet sur la base d'un historique concernant des achats et/ou d'exploration de projets par l'utilisateur et un moyen de commande d'affichage qui dans une première colonne d'affichage affiche, chronologiquement par date, les informations de projet extraites par le moyen d'extraction.
PCT/JP2014/080258 2014-11-14 2014-11-14 Système de recommandation, procédé de recommandation et programme de recommandation WO2016075826A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2016558840A JP6133518B2 (ja) 2014-11-14 2014-11-14 レコメンドシステム、レコメンド方法及びレコメンドプログラム
PCT/JP2014/080258 WO2016075826A1 (fr) 2014-11-14 2014-11-14 Système de recommandation, procédé de recommandation et programme de recommandation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2014/080258 WO2016075826A1 (fr) 2014-11-14 2014-11-14 Système de recommandation, procédé de recommandation et programme de recommandation

Publications (1)

Publication Number Publication Date
WO2016075826A1 true WO2016075826A1 (fr) 2016-05-19

Family

ID=55953939

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2014/080258 WO2016075826A1 (fr) 2014-11-14 2014-11-14 Système de recommandation, procédé de recommandation et programme de recommandation

Country Status (2)

Country Link
JP (1) JP6133518B2 (fr)
WO (1) WO2016075826A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003256701A (ja) * 2002-02-28 2003-09-12 Toshiba Corp 電子商取引サーバ及び電子商取引方法
JP2013015927A (ja) * 2011-06-30 2013-01-24 Rakuten Inc 情報提供装置、情報提供方法、情報提供プログラム及び記録媒体
JP2014056562A (ja) * 2012-08-17 2014-03-27 Konami Digital Entertainment Co Ltd 管理装置、サービス提供システム、管理装置の制御方法、及び、管理装置のプログラム。
JP2014149664A (ja) * 2013-01-31 2014-08-21 Konami Digital Entertainment Co Ltd 情報提供装置、情報提供システム、情報提供装置の制御方法、及び、情報提供装置のプログラム。

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003256701A (ja) * 2002-02-28 2003-09-12 Toshiba Corp 電子商取引サーバ及び電子商取引方法
JP2013015927A (ja) * 2011-06-30 2013-01-24 Rakuten Inc 情報提供装置、情報提供方法、情報提供プログラム及び記録媒体
JP2014056562A (ja) * 2012-08-17 2014-03-27 Konami Digital Entertainment Co Ltd 管理装置、サービス提供システム、管理装置の制御方法、及び、管理装置のプログラム。
JP2014149664A (ja) * 2013-01-31 2014-08-21 Konami Digital Entertainment Co Ltd 情報提供装置、情報提供システム、情報提供装置の制御方法、及び、情報提供装置のプログラム。

Also Published As

Publication number Publication date
JP6133518B2 (ja) 2017-05-24
JPWO2016075826A1 (ja) 2017-08-03

Similar Documents

Publication Publication Date Title
WO2020048084A1 (fr) Procédé et appareil de recommandation de ressource, dispositif informatique et support d'informations lisible par ordinateur
US20170235792A1 (en) Searching for entities based on trust score and geography
US10977318B2 (en) Search apparatus, search method, search program, and recording medium
JP6318536B2 (ja) 車両の相乗り者検索システム
US11176574B2 (en) Contextual secondary content for search results
JP5805548B2 (ja) 情報処理装置、及び、情報処理方法
US11544629B2 (en) Personalized merchant scoring based on vectorization of merchant and customer data
JP5373956B1 (ja) 情報処理装置、情報処理方法及び情報処理プログラム
US20140344275A1 (en) Information processing apparatus, information processing method, information processing program, and recording medium
US20100114615A1 (en) Optimized inventory selection
JP6229074B2 (ja) レコメンドシステム、レコメンド方法及びレコメンドプログラム
US20170365014A1 (en) Systems, methods and non-transitory computer readable storage media for tracking and evaluating predictions regarding relationships
KR101861828B1 (ko) 개인화 컨텐츠 제공 방법 및 이를 위한 컴퓨터 프로그램
JP6414192B2 (ja) 情報処理装置、情報処理方法、情報処理プログラム
US20140351088A1 (en) Computer-implemented real estate information delivery system and method
JP6133518B2 (ja) レコメンドシステム、レコメンド方法及びレコメンドプログラム
KR20200100432A (ko) 프리랜서 매칭 서버 및 이를 이용한 프리랜서 매칭 방법
JP2015135694A (ja) 情報選択装置、情報選択方法、およびコンピュータプログラム
JP5858127B2 (ja) 情報選択装置、情報選択方法、端末装置およびコンピュータプログラム
JP2020030469A (ja) 情報処理装置、情報処理方法、及び情報処理プログラム
JP6065061B2 (ja) 情報処理装置、端末装置、情報処理方法、表示方法、情報処理プログラム、及び表示プログラム
JP5743302B2 (ja) 情報選択装置、情報選択方法、端末装置およびコンピュータプログラム
JP2016081156A (ja) 顔写真表示装置
JP6102979B2 (ja) 情報選択装置、情報選択方法、およびコンピュータプログラム
JP2019204208A (ja) 情報処理装置、情報処理方法、及び情報処理プログラム

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14906003

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2016558840

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 14906003

Country of ref document: EP

Kind code of ref document: A1