JP2013093054A - Information providing system, terminal, information providing server and information providing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable a user to easily select content suitable to a state of that time from a list including information on a plurality of content.SOLUTION: An information providing server includes an object data storing part, a learning processing part for generating new learning data, a recommendation data generating part for generating recommendation data, and a recommendation data transmitting part for transmitting the recommendation data to a terminal. The terminal includes a use frequency data storing part, an order operating part for operating a display order about each content, and a display part for arranging titles of the content in an order determined by the display order operated by the order operating part to display the titles of the content.

Description

  The present invention relates to an information providing system, a terminal, an information providing server, and an information providing method having a terminal and an information providing server that recommends content to the terminal according to the preference of the terminal.

  In recent years, the provision of contents such as music data distribution and video data distribution using a network has become popular. For example, in such a system, contents of various genres and contents are provided from a content providing apparatus to a terminal owned by a user via a network, but the genres and contents of contents provided by the content providing apparatus increase. There is a tendency.

  For example, when a user intends to receive provision of necessary content, the user selects the necessary content from a list including a large number of content titles. Such a list of contents is devised so as to divide the contents by genre, or arrange them in the order of numbers assigned to the contents or in order of the dates when the contents are generated. On the other hand, it is difficult for the user to select necessary content from a list including a large amount of content information.

  On the other hand, there is a technique that facilitates selection of information by the user by searching for information preferred by the user based on preference information of information used by the user and providing the search result to the user (for example, , See Patent Document 1). In the technique described in Patent Document 1, preference is given to information used by a user belonging to a group formed by a plurality of users, not individual users, and a service is provided.

JP 2006-127370 A

  However, although the user's use of content (information) has a certain tendency, when content is always recommended based on the same preference information, content not required by the user may be recommended. That is, the content that the user needs is generally dependent on the situation in which the user is placed. For example, the content that the user expects depends on the location where the user exists, the current time, and the like.

  Therefore, an object of the present invention is to provide an information providing system, a terminal, an information providing server, and an information providing method that allow a user to easily select necessary content from a list including information on a plurality of contents.

  An information providing system according to an embodiment of the present invention includes a terminal and an information providing server that recommends content to the terminal according to the preference of the terminal. The information providing server includes a target data storage unit, a learning processing unit, a recommendation data generation unit, and a recommendation data transmission unit. The terminal includes a usage frequency data storage unit, a rank calculation unit, and a display unit. The target data storage unit stores target data associating a plurality of content titles usable by the terminal with usage conditions corresponding to the state of the terminal and a weight value corresponding to the state. The learning processing unit obtains a preference strength value representing the preference of the terminal related to the content based on the usage frequency data related to the content preference of the terminal received from the terminal and the learning data related to the history of selection of the content of the terminal. New learning data including a value is generated. The recommendation data generation unit generates recommendation data in which titles of a plurality of contents selected from target data based on the learning data are associated with recommendation ranks determined based on the preference intensity value and the weight value. The recommendation data transmission unit transmits the recommendation data to the terminal. The usage frequency data storage unit stores usage frequency data for associating usage conditions corresponding to the state of the terminal and a weight value corresponding to the state. The rank calculation unit includes the weight value associated with the condition that matches the terminal state data in the usage frequency data, and the weight value associated with the condition that matches the terminal state data in the recommendation data transmitted from the information providing server. The display order is calculated for each content by weighting each recommendation order included in the recommendation data. The display unit arranges and displays the titles of the contents in the order determined by the display order calculated by the ranking order calculation unit.

1 is an information providing system according to a first embodiment of the present invention. It is an example of the external view of the terminal shown in FIG. It is an example of the terminal state data which the terminal shown in FIG. 1 uses. It is an example of the input data which the terminal shown in FIG. 1 utilizes. It is an example of the setting data which the terminal shown in FIG. 1 uses. It is an example of the recommendation data which the terminal shown in FIG. 1 uses. It is an example of the metadata which the terminal shown in FIG. 1 uses. It is an example of the usage frequency data which the terminal shown in FIG. 1 uses. It is a flowchart explaining an example of the process in the terminal shown in FIG. It is a flowchart explaining an example of the process in the terminal shown in FIG. It is an example of the target data which the information provision server shown in FIG. 1 uses. It is an example of the metadata which the information provision server shown in FIG. 1 utilizes. It is an example of the recommendation data which the information provision server shown in FIG. 1 uses. It is an example of the learning data which the information provision server shown in FIG. 1 utilizes. It is an example of the terminal setting data which the information provision server shown in FIG. 1 uses. It is an example of the usage frequency temporary data which the information provision server shown in FIG. 1 uses. It is a flowchart explaining an example of the process in the proposal server shown in FIG. It is a flowchart explaining an example of the process in the provision server shown in FIG.

<Information provision system>
As shown in FIG. 1, an information providing system 1 according to the best embodiment of the present invention includes a terminal 10 owned by a user (user) and an information providing server capable of transmitting / receiving data to / from the terminal 10 via a network. 20. The information providing server 20 stores a large number of contents and a usage history of the terminal 10 or the user's content, and recommends the terminal 10 with a content that matches the preference of the terminal 10 or the user's content based on the usage history. .

  Here, “content” is described as digital data such as a web page, audio data, and image data. In the information providing system 1, the information providing server 20 may store these contents in an internal storage device and provide them to the terminal 10. Alternatively, the information providing server 20 only transmits a list of contents recommended to the terminal 10, and the terminal 10 acquires content selected from the list received from the information providing server 20 from a content providing server (not shown). It may be a configuration.

<Terminal>
As shown in FIG. 1, the terminal 10 includes a central processing unit (CPU) 110, a storage device 120 such as a magnetic disk, an input unit 130 such as an operation button for inputting an operation, a display unit 140 for displaying information, An information processing terminal such as a mobile phone or a PDA (Personal Digital Assistance) provided with a communication processing device 150 or the like that controls transmission / reception of data to / from the outside via a network.

  As illustrated in FIG. 1, the storage device 120 of the terminal 10 stores terminal state data 121, input data 122, setting data 123, recommendation data 124, metadata 125, and usage frequency data 126.

  Further, the storage device 120 stores a program (not shown) corresponding to the information providing system 1, and a terminal state detection unit that detects the terminal state data 121 by reading and executing the program. 111, a setting data transmission unit 112 that transmits setting data 123 to the information providing server 20, a recommendation data receiving unit 113 that receives recommendation data 124 transmitted from the information providing server 20, and metadata 125 from the recommendation data 124. The metadata generation unit 114 to be generated, the frequency data update unit 115 to update the usage frequency data 126, the frequency data transmission unit 116 to transmit the usage frequency data 126 to the information providing server 20, and the display priority of the data are calculated. And a ranking calculation unit 117 for displaying the data on the display unit 140 based on the calculated priority order. A display processing section 118 is mounted.

  The input unit 130 of the terminal 10 includes selection buttons 130a to 130d and a determination button 130e as in the example of the external view shown in FIG. In the terminal 10, each process is executed according to an operation input through the input unit 130. The input unit 130 shown in FIG. 2 is an operation button, but other forms may be used as long as an operation can be input like a touch panel or the like.

  The display unit 140 illustrated in FIG. 2 is an example in which a plurality of items including the title and description of the content received from the information providing server 20 are displayed. The title “Coffee Shop ABC” and the description “Cold Drink” An item consisting of “abundant ...” is selected as the attention area 140a. Note that only a part of the information can be displayed on the display unit 140 due to the limitation of the display width. Accordingly, a part of the explanatory text is omitted, and for example, as shown in the explanatory text “Abundant cold drinks ...” of the attention area 140a in FIG.

  For example, when the selection button 130a is selected in the state shown in FIG. 2, the attention area 140a is changed to an item including the title “Italian cuisine DEF”. Further, when the determination button 130e is selected while the attention area 140a is in the state of the item “coffee shop ABC”, the display unit 140 displays detailed data regarding “coffee shop ABC”.

  For example, as illustrated in FIG. 1, the terminal state detection unit 111 detects GPS data 121a that is current position information (latitude and longitude) of the terminal 10 using a GPS (global positioning system) technology or the like. 111a, an outside air temperature detector 111b that detects current outside air temperature data 121b from an external thermometer using a thermometer or network incorporated therein, and a clock or radio clock technology incorporated inside, etc. The time detection part 111c which detects this time data 121c is provided.

  Further, the terminal state detection unit 111 stores the detected terminal state data 121 in the storage device 120. Specifically, the terminal state detection unit 111 stores GPS data 121a, outside air temperature data 121b, and time data 121c as the terminal state data 121 in the storage device 120. Each of the detection units 111a to 101c acquires the data 121a to 102c at a predetermined timing such as a timing when an operation is input through the input unit 130 or a regular timing.

  As shown in FIG. 3A, the GPS data 121a includes “terminal ID” for identifying the terminal 10, “date and time” for “user ID” for identifying the user (user) using the terminal 10, “Latitude” and “longitude” are data associated with each other. According to the GPS data 121a shown in FIG. 3 (a), the terminal 10 moves to “latitude N35.39.18, longitude E139.42.17” at “August 22, 2007 14:24:37”. You can see that it existed.

  The outside air temperature data 121b is data in which “date and time” and “outside temperature” are associated with the “terminal ID” and the “user ID” as in the example illustrated in FIG. According to the outside air temperature data 121b shown in FIG. 3B, the outside air temperature of the place where the terminal 10 existed at “August 22, 2007 14:25:00” was “33.6 degrees”. I understand.

  The time data 121c is data in which “date and time” and “time” are associated with the “terminal ID” and the “user ID” as in the example illustrated in FIG.

  Here, description will be made assuming that position information represented by GPS data 121a, temperature information represented by outside air temperature data 121b, and time information represented by time data 121c are used as terminal state data 121. However, the information used as the terminal state data 121 is not limited to “position”, “temperature”, and “time”, but “acceleration”, “frequency”, “ambient volume”, “day of the week”, “humidity”, “ Various other information can be used as long as the information is used for selecting contents such as “climate” and “season”. Further, when such information is used as the terminal state data 121, the terminal state detection unit 111 is provided inside or outside in addition to the GPS detection unit 111a, the outside air temperature detection unit 111b, and the time detection unit 111c. It also has a function of detecting information such as acceleration and vibration frequency from the sensor.

  Input data 122 and setting data 123 input to the input unit 130 are stored in the storage device 120.

  In the input data 122, as in the example shown in FIG. 4, “terminal ID” for identifying the selected button is associated with “terminal ID” and “user ID”. Further, each “input device ID” is data associated with a plurality of “recommendation IDs” (m in FIG. 4) identifying “date and time” when the button is operated and recommendation data 124 selected by the operation. . Although the recommendation data 124 will be described in detail later, the recommendation data 124 is data including attributes of contents recommended by the information providing server 20 to the terminal 10, and is data received by the terminal 10 from the information providing server 20.

  Further, each “recommendation ID” includes “metadata ID” that is an identifier of the metadata 125 associated with the recommendation ID and items displayed on the display unit 140 by data specified by the metadata ID. A plurality (“n” in FIG. 4) of “integrated reaction times”, which is the number of times operated by the button identified by “input device ID”, is associated. Although the metadata 125 will be described later, the metadata 125 having the metadata ID “M104” specified by the metadata generation unit 114 in advance from the keyword of the recommendation data 124 indicated by the recommendation ID “R123”. When the content is extracted, the metadata specified by the metadata ID “M014” is associated with the recommendation ID “R123”.

  For example, it is assumed that an operation whose input ID is indicated by a “decision button” is executed when “coffee shop ABC rich in cold drinks” is displayed as the attention area 140a in FIG. . At this time, in the recommendation data 124 in which the recommendation ID displayed in the attention area 140a is indicated by “R123”, the metadata ID included in the attention area 140a is “M014” and the context is represented as “cold drink”. Since the data 125 exists, the metadata ID of the item of which the recommended ID is “R123” of the input data 122 whose input device ID is indicated by the “OK” button is expressed by “M014”. The cumulative response count corresponding to the item is incremented by one and recorded. That is, from the input data 122 shown in FIG. 4, the “OK” button has been operated “three times” for the item displayed based on the recommendation ID “R123” and the metadata ID “M014”. I understand.

  As illustrated in FIG. 5, the setting data 123 includes “setting date / time” in which a target item is set in “terminal ID” and “user ID”, and set content “setting content”. "Setting items" are associated. According to the setting data 123 shown in FIG. 5, it is understood that the value “unnecessary” is set for the parameter “pachinko parlor” on “June 12, 2007”. That is, the user has set that the contents of the pachinko parlor are unnecessary.

  The setting data transmission unit 112 reads the setting data 123 from the storage device 120 at a predetermined timing such as a timing when the setting data 123 is updated or a periodic timing, and transmits the setting data 123 to the information providing server 20 via the communication processing device 150. .

  Here, the communication processing device 150 is a general communication interface, and data transmission / reception between the terminal 10 and the information providing server 20 is controlled by the communication processing device 150.

  The recommendation data receiving unit 113 receives the recommendation data 124 transmitted from the information providing server 20 via the communication processing device 150. The recommendation data receiving unit 113 stores the received recommendation data 124 in the storage device 120.

  In the recommendation data 124, as shown in the example shown in FIG. 6, “Recommendation ID” attached to the content recommended by the information providing server 20 to the user is associated with “Terminal ID” and “User ID”. ing. Each “recommendation ID” includes the content “title”, “genre”, “description” describing the content, and the priority order of the content set by the information providing server 20 as the attributes of the recommended content. The “initial recommendation order”, the “person name” of the person related to the content, and the “keyword” of the content are associated.

  Further, in the recommendation data 124, “weight data” is associated with each “keyword”. Each “weight data” is associated with “GPS weight” including “condition” and “weight value”, “outside air temperature weight”, and “time weight”. When the terminal 10 displays the list of contents received from the information providing server 20, the contents are weighted by the terminal state data 121 to change the priority. The “weight value” displays the list of contents. This is a value given from the information providing server 20 as an index for determining the priority order of each content.

  In the present embodiment, since the position information, temperature information, and time information are used as the terminal state data 121, the “GPS weight” and the “outside temperature weight” are also corresponding to the recommendation data 124 in FIG. Although “time weight” is described, when other information is used as the terminal state data 121, the recommendation data 124 includes a weight corresponding to the other information. For example, when using acceleration information, the recommendation data 124 includes “acceleration weight”.

  According to the recommendation data 124 shown in FIG. 6, the title of data recommended with “R123” as the recommendation ID is “Coffee Shop ABC”, the genre of the data is “Restaurant”, and the description is “ Cold drinks are also delicious with a rich menu. ”, The initial recommendation ranking is“ 12th place ”, and the keyword is“ cold drink ”. Each recommendation data 124 can be associated with a plurality of keywords (n in FIG. 6). In addition to the keyword “cold drink” shown in FIG. 6, other keywords such as “delicious” are associated with each other. Yes. No person name is associated with the data with the recommendation ID “R123”.

  For example, in the recommendation data 124 of FIG. 6, the weight value when the GPS data is “N35.39.18, 1 km around E.139.42.17” is “1.2”, and the outside air temperature data is It can be seen that the weight value when “30 degrees or more” is “1.3”, and the weight value when the outside air temperature data is “10 degrees or less” is “1.1”.

  When the recommendation data 124 is stored in the storage device 120, the metadata generation unit 114 reads the recommendation data 124 from the storage device 120, extracts predetermined items from the recommendation data 124 according to a predetermined rule, and stores the metadata 125. Generate. Further, the metadata generation unit 114 stores the generated metadata 125 in the storage device 120.

  For example, the metadata generation unit 114 extracts a “context” such as a word included in the “description” of the recommendation data 124 using an existing technique such as morphological analysis, and extracts it as an example shown in FIG. In addition to attaching a “metadata ID” to each “context”, the metadata 125 is generated by associating the “context”, the “part of speech” of the context, the “superordinate concept information”, and the like. Further, in the metadata 125, as shown in an example shown in FIG. 7, “metadata ID” for identifying metadata is associated with “terminal ID” and “user ID”.

  Here, there are a plurality (q in FIG. 7) of recommendation data 124 including the same “context” in the explanatory text, and a plurality of “superordinate concept information” (m in FIG. 7) for each “context”. It may be. Also, it is considered that there are a plurality of “contexts” (l in FIG. 7).

  According to the metadata 125 shown in FIG. 7, the metadata ID “M014” includes the context “cold drink” included in the recommendation data 124 such as the recommendation IDs “R123” and “R456”, and the part of speech is “compound word”. It is understood that the superordinate concepts are “temperature”, “sense”, “food”, and the like.

  When a new operation is input to the input unit 130 and the input data 122 is updated, the frequency data update unit 115 reads the terminal state data 121, the input data 122, the metadata 125, and the usage frequency data 126 from the storage device 120. Thus, a new “weight value” is calculated. Further, the frequency data updating unit 115 updates the “weight value” of the usage frequency data 126 with a new “weight value”. The process of updating the usage frequency data 126 in the frequency data update unit 115 will be described in detail later.

  The usage frequency data 126 is data generated based on the history of content selection in the terminal 10, and is data regarding a state (current position, temperature, time, etc.) when a user using the terminal 10 likes the content. is there. As in the example illustrated in FIG. 8, in the usage frequency data 126, “metadata ID” and “higher level concept information ID” are associated with “terminal ID” and “user ID”. Each “metadata ID” is associated with a “context” specified by the metadata ID, a “part of speech” of the context, “superior concept information” of the context, and “weight data” of the context. Furthermore, “superior concept information” and “weight data” are associated with each “superior concept information ID”. Each “weight data” is associated with “GPS weight” including “condition” and “weight value”, “outside air temperature weight”, and “time weight”. This “weight data” is used when obtaining the priority for displaying the content. When the terminal status data 121 includes information other than position information, temperature information, and time information, the weight data of the usage frequency data 126 associates a weight corresponding to the information.

  According to the usage frequency data 126 illustrated in FIG. 8, the weight data associated with the metadata ID “M104” indicates that the user has a weight value of “0. 95 ". Similarly, it is “−0.37” when “5 degrees or less”.

  When the display operation of the recommended content list is input to the input unit 130, the rank calculation unit 117 reads the recommendation data 124, the metadata 125, and the usage frequency data 126 from the storage device 120, and specifies the recommendation data 124. The display priority of the content to be calculated is calculated. The rank calculation processing by the rank calculation unit 117 will be described in detail later.

  The display processing unit 118 generates a display screen by arranging the titles of the contents in accordance with the display priority order obtained by the rank calculation unit 117, and displays the display screen on the display unit 140. The examples described above in FIG. 2 are “Italy DEF Seafood is delicious ...”, “Coffee Shop ABC has a lot of cold drinks ...”, “XYZ Denki Time Sale, 10,000 yen discount now ...” It is an example displayed in this order.

《Usage frequency data update processing》
Here, an example of processing for updating the usage frequency data 126 in the frequency data update unit 115 will be described using the flowchart shown in FIG.

  When an operation is input to the input unit 130, the input unit 130 stores the input data 122 in the storage device 120 (S001).

  For example, when the recommendation data 124 shown in FIG. 6 and the metadata 125 shown in FIG. 7 are stored in the storage device 120 and the item including the title “Coffee Shop ABC” on the display unit 140 shown in FIG. 2 is the attention area 140a. It is assumed that the selection button 130e that is the input device ID “decision button” of the input unit 130 is pressed. At this time, in step S001, the input unit 130 includes the recommendation ID “R123” associated with the title “coffee shop ABC” in the recommendation data 124 and the context “cold” included in the description of the recommendation ID “R123” in the metadata 125. The metadata ID “M014” associated with “drink” is extracted. The input unit 130 updates the input data 122 in association with the “integrated reaction count” together with the input device ID “decision button”, the recommendation ID “R123”, and the metadata ID “M014”. If the input device ID “decision button”, the recommendation ID “R123”, the metadata “M014”, and the cumulative reaction count “3” are already associated as in the input data 122 shown in FIG. 130 stores the input data 122 updated to “4” by incrementing the value of the cumulative reaction count.

  When the input data 122 is stored in the storage device 120, the frequency data update unit 115 reads the input data 122, the terminal state data 121, and the usage frequency data 126 from the storage device 120 (S002).

  Thereafter, the frequency data updating unit 115 extracts “metadata ID” associated with the “number of integrated reactions” whose value has been updated in step S001, and “weight” associated with the “metadata ID” from the usage frequency data 126. Data "is extracted (S003).

  Subsequently, the frequency data update unit 115 compares each “condition” included in the weight data of the usage frequency data 126 extracted in step S003 with the value of the terminal state data 121, and the value of the terminal state data 121 satisfies “ "Condition" is searched (S004). Here, when there is a “condition” that the terminal state data 121 satisfies, all corresponding conditions are searched.

  Further, the frequency data update unit 115 extracts “weight values” associated with the “conditions” searched in step S004 from the weight data to be processed in step S004 (S005, S006).

  Thereafter, the frequency data updating unit 115 calculates a “new weight value” based on the “weight value” extracted in step S006 according to a predetermined method (S007), and sets the weight value of the frequency frequency data 126 to “ Update with “new weight value” (S008).

  The frequency data update unit 115 executes the processing from step S006 to S008 for all the “conditions” searched in step S004, and updates the usage frequency data 126.

  As an example of a method for calculating the “new weight value”, “a method in which a value obtained by changing the current weight value at a predetermined ratio according to the number of integrated reactions” is used as a new weight value can be considered. Specifically, in the input data 122, “when the cumulative reaction count associated with the metadata ID to be processed is 1 to 5, the value obtained by increasing the weight value by 3% is used as the new weight value. For example, when the cumulative reaction number is 6 to 10, a value obtained by increasing the weight value by 5% is used as a new weight value. When such a calculation method is determined, the storage device 120 associates the terminal state data 121 shown in FIG. 2, the input data 122 shown in FIG. 4, and the usage frequency data 126 shown in FIG. An example of processing for calculating a new weight value will be described.

  When the data operated by the determination button 130e of the input unit 130 is specified by the metadata ID “M014”, the frequency data update unit 115 sets the metadata ID “M014” from the usage frequency data 126 in step S003. Extract the associated weight data. If the outside air temperature data 121b is “33.6 degrees”, the frequency data updating unit 115 satisfies the condition “30 degrees or more” in order to satisfy the condition “30 degrees or more”. 0.95 "is extracted. Since the cumulative reaction count associated with the metadata ID “M014” from the input data 122 is “3”, the frequency data update unit 115 determines in step S007 that “weighting is performed when the cumulative response count is 1 to 5. A value “0.98” obtained by increasing the extracted weight value “0.95” by 3% is calculated as a new weight value according to the method of “the value obtained by increasing the value by 3% is used as a new weight value”.

  The frequency data updating unit 115 that has completed the processing of steps S006 to S008 extracts “higher level concept information” associated with the metadata ID that is the processing target in step S003 from the metadata 125 (S009). For example, when the metadata ID “M014” is extracted from the usage frequency data 126 of FIG. 8 in step S003, the frequency data update unit 115 sets the superordinate concept information “cold” associated with the metadata ID “M014” from the metadata 125. , “Temperature”, “Sense”, “Food”, etc. are extracted.

  In addition, the frequency data update unit 115 extracts “weight data” associated with the “higher concept information ID” that identifies the “higher concept information” extracted in step S005 from the usage frequency data 126 (S010).

  The frequency data update unit 115 performs the same processing as the above-described steps S005 to S008 in steps S010 to S014 on the “weight data” of each “higher level concept information” extracted in step S010, thereby creating new weight values. The usage frequency data 126 is updated.

  As described above, when input data is input by an operation on the terminal 10, the frequency data update unit 115 uses the terminal status data 121 when the terminal 10 selects content to transmit the information from the information providing server 20 to the terminal 10. The usage frequency data 126 used when determining the priority of display of the recommended content is updated.

《Rank calculation processing》
Next, an example of processing for calculating the display priority order in the order calculation unit 117 will be described using the flowchart shown in FIG.

  First, the rank calculation unit 117 reads terminal state data 121, recommendation data 124, metadata 125, and usage frequency data 126 from the storage device 120 (S101).

  Subsequently, the rank calculation unit 117 selects one “metadata ID” or “higher level concept information” included in each metadata 125 extracted from all the recommended data 124, and selects from the usage frequency data 126. The “weight data” associated with the “metadata ID” or “superior concept information ID” for identifying the superordinate concept information is extracted (S102). For example, when the metadata ID “M014” is selected from the metadata 125 in FIG. 7, the rank calculation unit 117 extracts weight data associated with the metadata ID “M014” from the usage frequency data 126.

  Next, the rank calculation unit 117 searches for “weight data” including the terminal state data 121 within the range of “condition” from the one “weight data” extracted in step S102, and searches the metadata 125 for the search. “Recommendation ID” associated with “metadata ID” or “superordinate concept information” associated with “weight data” is extracted (S104).

  Subsequently, the rank calculation unit 117 extracts “recommendation degree” associated with the one “recommendation ID” extracted in step S105 from the recommendation data 124 (S106). Further, the rank calculation unit 117 calculates a new “recommendation degree” from the “recommendation degree” extracted in step S105 according to a predetermined rule (S107). The rank calculation unit 117 stores the new “recommendation degree” obtained here in a memory (not shown) in association with the “recommendation ID”. For example, the rank calculation unit 117 obtains a new “recommendation degree” using the formula (1).

New recommendation level = (recommendation level) + (GPS weight value of recommendation data × GPS weight value of usage frequency data) + (outside temperature weight value of recommendation data × outside temperature weight value of usage frequency data) + (recommendation data (Time weight value × time weight value of usage frequency data) (1)
In the formula (1), the “recommendation degree” included in the recommendation data 124 and the product of each “weight value” included in the recommendation data 124 and each “weight value” included in the usage frequency data 126 are summed, A new “recommendation level”. For example, the recommendation degree “1.874” is extracted from the recommendation data 124 shown in FIG. 6, and the GPS weight value “1.2” and the outside air temperature weight value “1.3” are extracted as the corresponding weight data. Suppose that the time weight value is not extracted. Also, it is assumed that the GPS weight value “0.88” and the outside air temperature weight value “0.95” are extracted as the corresponding weight data from the usage frequency data 126 shown in FIG. 8, and the time weight value is not extracted. . At this time, the rank calculation unit 117 substitutes the value “4.165 (= 1.874 + 1.2 × 0.88 + 1.3 × 0.95)” obtained by substituting each extracted value into the equation (1). The new “recommendation degree” is stored in the memory.

  If a plurality of “recommended IDs” are extracted in step S104, the rank calculation unit 117 repeats the processes of steps S106 and S107 for all the extracted “recommended IDs” (S105). When a plurality of “weight data” are extracted in step S102, the rank calculation unit 117 executes the processes of steps S104 to S107 for all the extracted “weight data” (S103).

  When a new “recommendation level” is obtained for all “recommendation levels” corresponding to the recommendation data 124, the rank calculation unit 117 reads out the new “recommendation level” from the memory, and sets “recommendation level” in descending order. The “recommended IDs” are arranged, the ranks of “recommended IDs” are calculated and output to the display processing unit 118 (S108).

  The display processing unit 118 displays the titles of the contents included in the recommendation data 124 in the order calculated according to the terminal state data 121 by the rank calculation unit 117.

  In this way, when displaying the title of the content included in the recommendation data 124 received from the information providing server 20 in the terminal 10, the rank calculation unit 117 uses the terminal state data 121 representing the current state of the terminal 10. Are arranged in the priority order of the calculated contents.

  As described above, in the terminal 10, the content recommended from the information providing server 20 is determined in priority order based on the terminal state data 121 representing the current state of the terminal and the usage frequency data 126 representing the past usage history, and the titles are determined. The list of is displayed. In addition, the usage frequency data 126 used when setting the priority order is updated based on the usage history of the content and the terminal status data 121 representing the status when used.

  Therefore, when the terminal 10 displays the list of content recommended by the information providing server 20, not only the content preference of the terminal 20 but also the state (position, temperature, time) of the terminal 10 when the content has been used in the past. Etc.) are arranged in a priority order. This allows a user who uses the terminal 10 to select a content from a list of content titles arranged in a priority order that tends to be used in the current state. It is possible to easily select the content to be used.

<Information provision server>
The information providing server 20 is a device that includes a central processing unit (CPU) 210, a storage device 220 such as a magnetic disk, and a communication processing device 230 that controls transmission and reception of data with the outside via a network.

  As shown in FIG. 1, the storage device 220 of the information providing server 20 stores target data 221, metadata 222, recommendation data 223, learning data 224, terminal setting data 225, and usage frequency temporary data 226.

  The storage device 220 stores a program (not shown) for realizing the information providing server 20, and the metadata 222 is generated from the target data 221 by reading and executing this program. The metadata generation unit 211, the recommendation data generation unit 212 that generates the recommendation data 223, the recommendation data transmission unit 213 that transmits the recommendation data 223 to the terminal 10, the learning processing unit 214 that updates the learning data 224, and the terminal 10 The setting data receiving unit 215 that receives the setting data transmitted from the terminal 10 and stores it in the storage device 220 as the terminal setting data 223, and the usage frequency data transmitted from the terminal 10 and receives the usage frequency data 226 as the temporary storage frequency data 226. And a frequency data receiving unit 216 for storing.

  In the target data 221, as shown in the example shown in FIG. 11, “Recommendation ID” attached to the content recommended to the user identified by the user ID is added to “Terminal ID” and “User ID”. Associated. Each “recommended ID” is associated with “title” and “genre” of the recommended content, “description” describing the content, “person name” of the person related to the content, and “keyword” of the content. ing. Furthermore, “weight data” is associated with each “keyword”. Each “weight data” is associated with “GPS weight” including “condition” and “weight value”, “outside air temperature weight”, and “time weight”.

  The metadata generation unit 211 reads the target data 221 from the storage device 220 at a predetermined timing such as a timing at which the target data 221 is updated or a periodic timing, and performs predetermined items according to a predetermined rule from the read target data 221. And metadata 222 is generated. Further, the metadata generation unit 211 stores the generated metadata 222 in the storage device 120.

  For example, the metadata generation unit 211 extracts a “context” such as a word included in the “description” of the target data 221 by using an existing technique such as morphological analysis, and extracts “context” for each extracted “context”. With the “metadata ID” attached, the metadata 125 is generated by associating “context”, “part of speech” and “superordinate concept” as shown in FIG.

  The recommendation data generation unit 212 reads the target data 221, the metadata 222, the learning data 224, and the terminal setting data 225 from the storage device 220 at a predetermined timing such as a timing at which the metadata 222 is updated or a periodic timing. Recommendation data 223 is generated. The process of generating the recommendation data 223 in the recommendation data generation unit 212 will be described in detail later.

  As shown in the example of FIG. 13, the recommendation data 223 includes “title”, “genre”, “description”, “initial recommendation rank”, “degree of recommendation”, “person” of the recommended content in the “recommendation ID”. “Name” and “Keyword” are associated with each other.

  Here, the “initial recommendation order” is the order when the information providing server 20 recommends the content specified by the “recommendation ID” to the terminal 10 specified by the “terminal ID”. Similarly, the “recommendation degree” is a recommendation level when the content is recommended to the terminal 10.

  The recommendation data transmission unit 213 reads the recommendation data 223 from the storage device 220 and transmits it to the terminal 10 via the communication processing device 230 when a transmission request is issued from the terminal 10 at an appropriate timing or a predetermined timing such as periodically. To do. Note that “appropriate timing” refers to, for example, a time set in advance by the user so as to call the information providing server 20 to send a transmission request at a predetermined time, or a predetermined GPS data in the terminal 10. In this case, it is a timing at which a condition value preset in the terminal 10 by the user, such as GPS data preset to make a transmission request to the information providing server 20, is satisfied.

  The learning data 224 is data generated based on a history of content selection in the terminal 10, and is data regarding a tendency of a state in which the user using the terminal 10 uses the content (current position, temperature, time, etc.). is there. As an example illustrated in FIG. 14, in the learning data 224, “metadata ID” and “higher concept information ID” are associated with “terminal ID” and “user ID”. In addition, each “metadata ID” includes “context” specified by the metadata ID, “part of speech” of the context, “superior concept information” of the context, and content of the content corresponding to the user who uses the terminal 10. The “preference intensity value” representing the preference and the “weight data” of the context are associated with each other. Furthermore, “higher level concept information”, “preference intensity value”, and “weight data” are associated with each “higher level concept information ID”. Each “weight data” is associated with “GPS weight” including “condition” and “weight value”, “outside air temperature weight”, and “time weight”. This weight data is used as an index for determining the priority when recommending content.

  The learning processing unit 214 reads the learning data 224 and the temporary usage frequency data 226 from the storage device 220 at a predetermined timing such as a timing when the usage frequency temporary data 226 is updated or a periodic timing, and generates new learning data 224. Then, new learning data 224 is stored in the storage device 220.

  When the setting data is transmitted from the terminal 10, the setting data receiving unit 215 receives the setting data via the communication processing device 230 and stores it in the storage device 220 as the terminal setting data 225.

  The terminal setting data 225 associates “setting items” including “setting date” and “setting contents” with “terminal ID” and “user ID”. The terminal setting data 225 is the same data as the setting data 123 stored in the storage device 120 of the terminal 10 described above with reference to FIG.

  When the usage frequency data 126 is transmitted from the terminal 10, the frequency data receiving unit 216 receives the data via the communication processing device 230 and stores it as the usage frequency temporary data 226 in the storage device 220.

  The temporary usage frequency data 226 is recent data relating to the tendency of the user of the terminal 10 to use the content. As in the example illustrated in FIG. 16, in the usage frequency temporary data 226, “metadata ID” and “higher level concept information ID” are associated with “terminal ID” and “user ID”. Each “metadata ID” is associated with “context”, “part of speech”, “superior concept information”, and “weight data”. Furthermore, “superior concept information” and “weight data” are associated with “superior concept information ID”. This usage frequency temporary data 226 is the same data as the usage frequency data 126 stored in the storage device 220 of the terminal 10 described above with reference to FIG.

  Since the usage frequency temporary data 226 is data related to the latest trend, when the new usage frequency temporary data 226 is received by the frequency data receiving unit 216, the new usage frequency temporary data 226 is overwritten. Further, the usage frequency temporary data 226 may be deleted from the storage device 220 when read by the learning processing unit 214.

《Recommendation data generation processing》
Here, an example of processing for generating the recommendation data 223 in the recommendation data generation unit 212 will be described using the flowchart shown in FIG.

  First, the recommendation data generation unit 212 reads target data 221, metadata 222, learning data 224, and terminal setting data 225 from the storage device 220 (S301).

  The recommendation data generation unit 212 compares each metadata ID included in the metadata 222 with each metadata ID included in the learning data 223, and searches for a matching “metadata ID” (S302). In step S302, a plurality of “metadata IDs” may be searched.

  Thereafter, the recommendation data generation unit 212 extracts a “preference intensity value” associated with the one “metadata ID” searched in step S302 from the learning data 224 (S303, S304). Further, the recommendation data generation unit 212 searches the metadata 222 for a “recommendation ID” associated with the target “metadata ID” (S305). In step S305, a plurality of “recommended IDs” may be searched.

  When the “recommendation ID” is searched, the recommendation data generation unit 212 extracts the contents of each item associated with the one “recommendation ID” searched in step S 305 from the target data 221, and extracts the contents of the new recommendation data 223. The contents are added (S307). At this time, since the target data 221 does not include the “recommendation level”, the “recommendation level” of the recommendation data 233 is associated with “0” as an initial value.

  Subsequently, the recommendation data generation unit 212 calculates the sum of the current “recommendation level” value and the “preference intensity value” value extracted in step S304, and sets the calculated value as the “new recommendation level”. The “recommendation degree” of the recommendation data 223 is updated (S308). When the recommendation level is the initial value, the initial value is “0”, and thus the “new recommendation level” value is the “preference intensity value” value.

  When a plurality of “recommended IDs” are searched in step S305, steps S307 and S308 are repeated for each “recommended ID” (S306), and a plurality of “metadata IDs” are searched in step S302. If so, steps S304 to S308 are repeated for each "metadata ID" (S303).

  When the “recommendation degree” is calculated, the recommendation data generation unit 212 adjusts the “recommendation degree” according to a rule set in advance in the terminal setting data 225 (S309). For example, as shown in FIG. 15, when the parameter “Pachinko parlor” and the value “unnecessary” are set as the “setting contents” of the terminal setting data 225, the “terminal ID” associated with this “setting item” is used. This means that the terminal 10 to be identified does not require content related to the pachinko parlor. Therefore, the recommendation data generation unit 212 adjusts the recommendation level such as changing the “recommendation level” associated with the content related to the pachinko parlor to “0”.

  Thereafter, the recommendation data generation unit 212 generates the recommendation data 223 by setting the recommendation level in descending order as the “initial recommendation order” (S310).

  As described above, when the recommendation data generation unit 212 of the information providing server 20 generates the recommendation data 223 for recommending the content to the terminal 10, the recommendation data generation unit 212 uses the terminal setting data 225 together with the terminal 10 learning data 224. The priority order of contents that tend to be used on the terminal 10 is determined.

<< Learning data generation process >>
Next, an example of processing for generating learning data 224 in the learning processing unit 214 will be described using the flowchart shown in FIG.

  First, the learning processing unit 214 matches each “terminal ID” and “user ID” from the “metadata ID” included in the learning data 224 and the “metadata ID” included in the usage frequency temporary data 226. “Metadata ID” is searched (S403).

  When the matching “metadata ID” is searched in step S403, the learning processing unit 214 extracts “weight data” associated with each searched “metadata ID” from the learning data 224 (S404).

  Further, the learning processing unit 214 searches for “weight data” in which “condition” matches “weight data” included in the usage frequency temporary data 226 from the “weight data” extracted in step S404 (S405).

  Thereafter, the learning processing unit 214 calculates a new “weight value” for the “weight value” included in the “weight data” searched in step S405 according to a predetermined rule (S405). For example, the learning processing unit 214 obtains a new “weight value” using Expression (2).

New weight value = (weight value of current learning data × (number of learning times−1) + weight value of temporary data of usage frequency) / number of learning times (2)
In Equation (2), the “number of learnings” is the number of times that the learning processing unit 214 has executed a process of calculating a new “weight value” for the same weight data in the past. (Not shown). Alternatively, the number of learnings may be stored in the learning data 224. For example, the outside air temperature weight value “0.95” is extracted from the learning data 224 (current learning data) shown in FIG. 14, and the outside air temperature weight value “0.6” is extracted from the temporary use frequency data 226 in FIG. It is assumed that the current learning count is “31”. At this time, the learning processing unit 214 substitutes a value “0.939 (= (0.95 × 30 + 0.6) ÷ 31”) obtained by substituting these values into the equation (2) as a new “weight value”. Asking.

  Subsequently, the learning processing unit 214 performs the processing of steps S402 to S407 on the “metadata ID” of all the usage frequency temporary data 226, and recalculates a new weight value. That is, the learning processing unit 214 calculates a “preference intensity value” according to a predetermined rule, and updates the learning data 224 (S407). For example, the learning processing unit 214 calculates the “preference intensity value” using Expression (3).

Preference intensity value = {(GPS weight value × α) + (outside air temperature weight value × β) + (time weight value × γ)} (3)
In Expression (3), α, β, and γ are predetermined coefficients, which are unique values set in the information providing server 20. For example, α = 0.03 and β = 0.23 are set, and the GPS weight value “0.88”, the outside air temperature weight value “0.95”, and the time weight from the learning data 224 shown in FIG. It is assumed that the value “0.32” is extracted. At this time, the learning processing unit 214 substitutes these values into the equation (2), and the “preference intensity value” is calculated as 0.88 × 0.03 + 0.95 × 0.23 ++++ 0.32 × 0.02. The learning data 224 is updated.

  When the processing of steps S402 to S407 is completed for all the “metadata IDs” included in the learning data 224, all “higher level concept information” included in the learning data 224 is identified in steps S408 to S413. A similar process is executed for the “higher concept information ID”. Each process of steps S408-S413 is the same as that of steps S402-407 mentioned above.

  As described above, the learning processing unit 214 of the information providing server 20 updates the learning data 224 used when the recommendation data 223 is generated based on the usage frequency temporary data 226 related to the use of the content in the terminal 10.

  As described above, the information providing server 20 uses the learning data 224 related to the use of content in the terminal 10 when recommending the content to the terminal 10. Further, the learning data 224 is updated based on the usage frequency temporary data 226 that is updated at any time while the content is used in the terminal 10.

  Therefore, when the information providing server 20 generates the recommendation data 223, the priority obtained by reflecting the state (position, temperature, time, etc.) of the terminal 10 when the user of the terminal 10 has used the content in the past. Is included. This facilitates selection of content on the terminal 10.

  As described above, according to the information providing system 1 according to the best embodiment of the present invention, the information providing server takes into account the state when the terminal 10 has used the content in the past and the current state. The priority order is determined for the content recommended from 20, and a list of content titles is displayed according to the determined priority order. Thereby, the user can easily select content to be used from the titles displayed on the display unit 140 of the terminal 10. In other words, it is possible for the user to easily select content suitable for the current state (environment in which the user exists) from a list including information on a plurality of contents.

DESCRIPTION OF SYMBOLS 1 ... Information provision system 10 ... Terminal 111 ... Terminal state detection part 111a ... GPS detection part 111b ... Outside temperature detection part 111c ... Time detection part 112 ... Setting data transmission part 113 ... Recommended data reception part 114 ... Metadata generation part 115 ... Frequency data update unit 116 ... Frequency data transmission unit 117 ... Rank calculation unit 118 ... Display processing unit 120 ... Storage device (terminal state data storage unit, usage frequency data storage unit)
121 ... Terminal state data 121a ... GPS data 121b ... Outside temperature data 121c ... Time data 122 ... Input data 123 ... Setting data 124 ... Recommendation data 125 ... Metadata 126 ... Use frequency data 130 ... Input unit 130a ... Select buttons 130a-103d ... Selection button 130e ... Determination button 140 ... Display unit 140a ... Area of interest 150 ... Communication processing device 20 ... Information providing server 211 ... Metadata generation unit 212 ... Recommended data generation unit 213 ... Recommended data transmission unit 214 ... Learning processing unit 215 ... Setting data receiving unit 216... Frequency data receiving unit 220... Storage device (target data storage unit, learning data storage unit, usage frequency temporary data storage unit)
221 ... Target data 222 ... Metadata 223 ... Recommended data 224 ... Learning data 225 ... Terminal setting data 226 ... Usage frequency temporary data 230 ... Communication processing device

Claims (27)

An information providing system comprising a terminal and an information providing server that recommends content to the terminal,
The information providing server includes:
A target data storage unit that stores target data that associates content that can be used by the terminal with a use condition corresponding to the state of the terminal and a weight value corresponding to the use condition;
A learning processing unit for obtaining a preference intensity value representing a preference about the content based on the usage frequency data about the preference of the content received from the terminal, and generating learning data regarding the tendency of the state of using the content;
A recommendation data generating unit that generates recommendation data in which content and recommendation rank are associated with each other based on the learning data from the target data;
With
The terminal
A usage frequency data storage unit that stores usage frequency data associating usage conditions corresponding to the state of the terminal and a weight value corresponding to the state;
The recommendation included in the recommendation data by using the weight value in the usage frequency data and the weight value in the recommendation data transmitted from the information providing server, which are associated with the condition corresponding to the state of the terminal. A rank calculation unit that calculates the display rank of each content by weighting the rank;
A display unit for arranging and displaying the content in an order determined by the display order calculated by the rank calculation unit;
An information providing system comprising: When any content is selected from the content recommended by the recommendation data, a new weight value obtained by calculating the weight value associated with the usage frequency data based on the terminal state data indicating the state of the terminal A frequency data update unit for updating the usage frequency data by updating to
The information providing system according to claim 1. The usage frequency data includes a context representing content attributes and the weight value associated with the context.
The information providing system according to claim 1, wherein the rank calculation unit calculates the display rank using the weight value associated with the context. The information providing system according to claim 3, wherein the usage frequency data is associated with the weight value with respect to the higher-level concept information of the context as an attribute of the content. A terminal that can be connected to an information providing server that recommends content,
A usage frequency data storage unit that stores usage frequency data that associates content with usage conditions corresponding to the state of the environment of the terminal and weight values corresponding to the usage conditions;
In the usage frequency data, the terminal state data in the recommendation data in which the weight value associated with the terminal state data representing the state of the environment in which the terminal currently exists is associated with the content received from the information providing server and the recommendation order. A ranking calculation unit that calculates the display rank of content by weighting each recommendation rank included in the recommendation data using a weight value associated with
A display unit that arranges and displays the titles of the content in an order determined by the display order calculated by the rank calculation unit;
A terminal equipped with. An input unit for selecting any content recommended in the recommendation data;
A new weight obtained by calculating a weight value corresponding to the terminal state data for the content selected by the input unit from the usage frequency data at a predetermined ratio based on the terminal state data indicating the terminal state A frequency data update unit that updates the usage frequency data by updating to a value;
The terminal according to claim 5. The usage frequency data has a context that represents an attribute of the content, and the weight value can be associated with the context,
The terminal according to claim 5. In the usage frequency data, the weight value can be associated with the higher-level concept information of the context representing the content, as the content attribute,
The terminal according to claim 7.   The terminal according to any one of claims 5 to 7, wherein the terminal state data includes at least one of position data, outside air temperature data, time data, acceleration data, volume data, and vibration frequency. An information providing server that recommends content to connectable terminals,
A target data storage unit that stores target data that associates content that can be used by the terminal, a usage condition corresponding to the state of the terminal, and a weight value corresponding to the usage condition;
A learning processing unit that obtains a preference intensity value that represents a preference of the terminal related to the content based on usage frequency data related to the preference of the content received from the terminal, and generates learning data relating to a tendency of the state of using the content;
A recommendation data generating unit that generates recommendation data in which content and recommendation rank are associated with each other based on the learning data from the target data;
An information providing server comprising: The usage frequency data has a context representing content attributes;
Each context is associated with the weight value,
The information providing server according to claim 10, wherein the rank calculation unit calculates the display rank using the weight value associated with the context. When any content is selected from the content recommended by the recommendation data by the terminal, a value of the weight value associated with the context, which is a condition that matches the terminal state data, is determined from the usage frequency data. A frequency data update unit for updating the usage frequency data by updating to a new weight value calculated by increasing at a predetermined rate,
The information providing server according to claim 11, further comprising: An information providing method executed in an information providing server that recommends content to a terminal,
Using the weight value of the usage frequency data associated with the content received from the terminal, the usage condition and the weight value, a new weight value for the state of the terminal using the content is calculated to identify the preference regarding the content Calculating a preference intensity value to be
Generating learning data associating content usable by the terminal, a calculated preference intensity value, a usage condition corresponding to a state of the terminal using the content, and a calculated weight value;
Generating recommendation data associating content selected from target data based on the learning data with a recommendation rank determined based on the preference intensity value and the weight value;
A method for providing information. An information providing method executed in a terminal connectable with the information providing server,
When the recommendation data is received from the information providing server, the weight value associated with the terminal state data representing the state of the terminal from the usage frequency data stored in the usage frequency data storage unit, and the recommendation transmitted from the information providing server Calculating a display rank for content using a weight value associated with the terminal state data in the data;
Arranging the contents in an order determined by the calculated display order and displaying them on a display unit;
A method for providing information. Selecting any content recommended in the recommendation data;
When any one of the contents is selected, searching for a condition that is associated with the attribute of the selected content from the usage frequency data and matches the terminal state data;
Updating the usage frequency data by updating the weight value associated with the searched condition to a new weight value calculated by increasing at a predetermined rate;
The information providing method according to claim 14. The information providing method according to claim 14, wherein the usage frequency data includes content as an attribute of the content, and the weight value is associated with each context. In the step of calculating the display order, the display order is calculated using the weight value associated with the superordinate concept,
The information provision method according to claim 15, wherein in the terminal, in the step of calculating a new weight value, the new weight value is calculated for the weight value associated with the superordinate concept information.   The information providing method according to any one of claims 14 to 16, wherein the terminal state data includes at least one of position data, outside air temperature data, time data, acceleration data, volume data, and vibration frequency. An information providing system having a terminal and an information providing server that recommends content to the terminal according to the preference of the terminal,
The information providing server includes:
Target data for storing target data associating titles of a plurality of contents usable by the terminal, attributes of the contents, usage conditions corresponding to the state of the environment in which the terminal exists and weight values corresponding to the state A storage unit;
A frequency data receiving unit that receives usage frequency data associating content attributes, usage conditions, and weight values from the terminal;
Using the weight value of the usage frequency data, a new weight value is calculated for the state of the environment where the terminal that uses the content is present, and a preference strength value that specifies the preference of the terminal regarding the content is calculated. A learning processing unit that generates learning data associating the attribute, the calculated preference intensity value, the usage condition corresponding to the state of the environment in which the terminal using the content exists, and the calculated weight value;
A recommendation that generates recommendation data in which titles of a plurality of contents selected from the target data based on the learning data, attributes of the contents, and recommendation ranks determined based on the preference intensity value and the weight value are associated A data generator;
A recommendation data transmission unit for transmitting the recommendation data to the terminal;
With
The terminal
A terminal state data storage unit for storing terminal state data representing the state of the environment in which the terminal currently exists;
A usage frequency data storage unit that stores usage frequency data that associates content attributes with usage conditions corresponding to the state of an environment in which a terminal that uses the content exists and a weight value corresponding to the state;
A recommendation data receiving unit that receives the recommendation data transmitted from the information providing server;
Included in the recommendation data using the weight value associated with the condition matching the terminal state data in the usage frequency data and the weight value associated with the condition matching the terminal state data in the recommendation data A ranking calculation unit that weights each recommendation rank and calculates a display rank for each content;
A display unit that arranges and displays the titles of the content in an order determined by the display order calculated by the rank calculation unit;
A frequency data transmitting unit that transmits the usage frequency data to the providing server;
An information providing system comprising: In the usage frequency data, as a content attribute, the weight value is associated with the context representing the content and the higher-level concept information representing the context,
The rank calculation unit calculates the display rank using the weight value associated with the context and the weight value associated with the superordinate concept,
When any content is selected by an operation signal for selecting any content recommended in the recommendation data, the terminal status data is associated with the attribute of the selected content from the usage frequency data. And the weight value associated with the context that is the retrieved condition and the weight value associated with the higher-level concept information are increased and calculated at a predetermined rate. A frequency data update unit that updates the usage frequency data by updating to a new weight value;
The information provision system according to claim 19. A terminal connected to an information providing server that recommends content according to preferences,
A terminal state data storage unit for storing terminal state data representing the state of the environment in which the terminal currently exists;
A usage frequency data storage unit that stores usage frequency data that associates content attributes with usage conditions corresponding to the state of an environment in which a terminal that uses the content exists and a weight value corresponding to the state;
A recommendation data receiving unit for receiving recommendation data in which titles of a plurality of contents transmitted from the information providing server, attributes of the contents, and recommendation orders are associated;
Included in the recommendation data using the weight value associated with the condition matching the terminal state data in the usage frequency data and the weight value associated with the condition matching the terminal state data in the recommendation data A ranking calculation unit that weights each recommendation rank and calculates a display rank for each content;
A display unit that arranges and displays the titles of the content in an order determined by the display order calculated by the rank calculation unit;
A frequency data transmitting unit that transmits the usage frequency data to the providing server;
An input unit for inputting an operation signal for selecting any content recommended by the recommendation data;
When any one of the contents is selected by the operation signal, the condition that is associated with the attribute of the selected content and matches the terminal state data is searched from the usage frequency data, and the searched condition A frequency data updating unit that updates the usage frequency data by updating the weight value associated with the new weight value calculated by increasing at a predetermined rate;
A terminal equipped with. In the usage frequency data, as a content attribute, the weight value is associated with the context representing the content and the higher-level concept information representing the context,
The rank calculation unit calculates the display rank using the weight value associated with the context and the weight value associated with the superordinate concept,
The terminal according to claim 21, wherein the frequency data update unit calculates the new weight value for the weight value associated with the context and the weight value associated with the superordinate concept information.   The terminal status data is at least one of position data, outside air temperature data, time data, acceleration data, sound volume data, and vibration frequency. In each data including a weight value, the position, outside air temperature, time, acceleration, sound volume 23. The terminal according to claim 21, wherein a frequency range is included as the condition, and a weight value is associated with the range condition. An information providing method using an information providing system including a terminal and an information providing server that recommends content to the terminal according to a preference of the terminal,
In the information providing server, receiving usage frequency data associating content attributes, usage conditions, and weight values from the terminal;
In the information providing server, a preference intensity value for calculating a new weight value for the state of an environment in which a terminal using the content exists using a weight value of the usage frequency data and specifying a preference of the terminal regarding the content Calculating steps,
In the information providing server, a step of generating learning data associating content attributes, calculated preference intensity values, usage conditions corresponding to an environment state of the terminal using the content, and calculated weight values When,
When the learning data is generated in the information providing server, a plurality of content titles that can be used by the terminal from the target data storage unit, attributes of the content, usage conditions corresponding to an environment state in which the terminal exists, and Reading target data to associate with a weight value corresponding to the state;
In the information providing server, the titles of the plurality of contents selected from the target data based on the learning data, the attributes of the contents, and the recommendation order obtained based on the preference intensity value and the weight value are associated with each other. Generating recommendation data; and
Transmitting the recommendation data to the terminal in the information providing server;
In the terminal, receiving recommendation data transmitted from the information providing server;
When the terminal receives the recommendation data, it associates the content attribute from the usage frequency data storage unit with the usage condition corresponding to the state of the environment where the terminal using the content exists and the weight value corresponding to the state. Reading usage frequency data;
In the terminal, in the new usage frequency data read from the usage frequency data storage unit, the weight value associated with a condition that matches the terminal state data representing the state of the environment in which the terminal currently exists, and the information providing server Using the weight value associated with the condition that matches the terminal status data in the transmitted recommendation data, weighting each recommendation rank included in the recommendation data, and calculating a display rank for each content;
In the terminal, arranging the titles of the content in an order determined by the calculated display order and displaying them on the display unit;
In the terminal, transmitting new usage frequency data to the providing server;
A method for providing information. In the terminal, inputting an operation signal for selecting any content recommended by the recommendation data;
In the terminal, when any one of the contents is selected by the operation signal, searching for a condition that is associated with the attribute of the selected content from the usage frequency data and matches the terminal state data; ,
Updating the usage frequency data by updating the weight value associated with the searched condition in the terminal to a new weight value calculated by increasing at a predetermined rate;
The information providing method according to claim 24, comprising: In the usage frequency data, as a content attribute, the weight value is associated with the context representing the content and the higher-level concept information representing the context,
In the terminal, in the step of calculating the display order, the display order is calculated using the weight value associated with the context and the weight value associated with the superordinate concept,
26. In the step of calculating a new weight value in the terminal, the new weight value is calculated for the weight value associated with the context and the weight value associated with the superordinate concept information. Information providing method described.   The terminal status data is at least one of position data, outside air temperature data, time data, acceleration data, sound volume data, and vibration frequency. In each data including a weight value, the position, outside air temperature, time, acceleration, sound volume 27. The information providing method according to claim 24, wherein a frequency range is included as the condition, and a weight value is associated with the range condition.