JP7580502B2 - Suggestion device, suggestion method, and program - Google Patents

Description

The present invention relates to a suggestion device, a suggestion method, and a program.

Conventionally, there is known a technology for providing content based on user attributes. This type of technology is described, for example, in Patent Document 1. The interest analysis device described in Patent Document 1 is described as calculating the importance of content based on user attribute information and interest information indicating that the user has shown interest in the content. In addition, the travel support system described in Patent Document 2 is described as creating a proposed plan for the user's travel, including items on tourist destinations to be passed through and used, means of transportation, and routes, based on input information including condition information from the user terminal and user attribute information, in order to support the user's travel.

Patent Publication No. 2021-157206 JP 2016-139263 A

The technology described in Patent Document 1 calculates the importance of content based on user attribute information, while the technology described in Patent Document 2 describes creating a proposed plan based on user attribute information. However, the technologies described in Patent Documents 1 and 2 provide content based on input information including user attribute information and condition information from the user terminal, so there is a problem in that even if a list of searched content is suggested, it is difficult to convey to the user the reason for the suggestions.

The present invention has been made in consideration of the above problems, and aims to provide a suggestion device, a suggestion method, and a program that can improve the persuasiveness of a list of suggested content.

(1) One aspect of the present invention includes an estimation content providing unit that provides a plurality of estimation content including images and catch phrases specialized for an application field, which is one field in which a suggestion device realizes an information provision service; an operation receiving unit that receives a user's operation to select one of the plurality of estimation content; a value estimation unit that estimates values of a plurality of users in the application field based on the operation received by the operation receiving unit; a scene situation estimation unit that estimates a scene or situation from information including a spot identified from location information of a user, a person accompanying the user, a time period, and weather; a reception unit that receives a search request from a user; and a search unit that searches for content based on the search request received by the reception unit. a suggestion unit that calculates a higher score distribution for content based on each of the user's multiple values, calculates a higher score distribution for content based on a scene or situation for content that has a content attribute that matches an estimated scene or situation for the user, and transmits to the user terminal device content including a list of content that has a high score, among the content searched by the search unit, which is obtained by adding up the score distribution for content based on each of the user's multiple values and the score distribution for content based on the scene or situation in which the user is placed, wherein the suggestion unit creates a reason for each content included in the list of content based on the score distribution, and transmits information indicating the reason to the user terminal device of the user.

(2) One aspect of the present invention includes the steps of: providing a plurality of estimation contents including images and catch phrases specialized for an application field, which is one field in which a suggestion device realizes an information provision service; receiving a user's operation to select one of the plurality of estimation contents; estimating the values of a plurality of users in the application field based on the received operation; estimating a scene or situation in which the user is placed based on information including a spot identified from user's location information, the user's companion, the time of day, and the weather; receiving a search request from a user; searching for content based on the search request; and determining whether the content has a content attribute that matches the values of the user, and whether the content has a plurality of values of the user. a step of calculating a higher score metric for content based on each of the user's multiple values, calculating a higher score metric for content based on a scene or situation for content that has a content attribute that matches the scene or situation estimated for the user, and transmitting content including a list of content that has a high score among the searched contents, the total score metric for content based on each of the user's multiple values and the total score metric for content based on the scene or situation in which the user is placed, to a user terminal device; and a step of creating a reason for each content included in the list of contents based on the score metric, and transmitting information indicating the reason to the user terminal device of the user.

(3) One aspect of the present invention includes the steps of providing a computer of an information processing device with a plurality of estimation contents including images and catch phrases specialized for an application field, which is one field in which a suggestion device realizes an information provision service; receiving a user's operation to select one of the plurality of estimation contents; estimating the values of a plurality of users in the application field based on the received operation; estimating a scene or situation in which the user is placed based on information including a spot identified from user's location information, the user's companion, the time of day, and the weather; receiving a search request from a user; searching for content based on the search request; and selecting a content having a content attribute that matches the values of the user. a step of calculating a higher score total for content based on each of the user's multiple values, calculating a higher score total for content based on a scene or situation for which the content has content attributes that match the scene or situation estimated for the user, and transmitting to the user terminal device content including a list of content that has a high score among the searched contents, the total score total for content based on each of the user's multiple values and the total score total for content based on the scene or situation in which the user is placed; and a step of creating a reason for each content included in the list of contents based on the score total, and transmitting information indicating the reason to the user terminal device of the user.

According to one aspect of the present invention, it is possible to improve the persuasiveness of the list of suggested content.

1 is a block diagram showing a configuration example of a suggestion system according to an embodiment. FIG. 2 is a diagram illustrating an example of an overall process of a suggestion system according to an embodiment. FIG. 2 is a diagram showing an example of content for value estimation in the embodiment. 11 is a diagram for explaining an example of a process for updating a user's sense of values based on the user's behavior in an embodiment. FIG. 11 is a diagram for explaining a process of updating a value by a suggestion device according to an embodiment. FIG. 11 is a flowchart showing an example of a process for making suggestions according to an embodiment. FIG. 2 is a diagram for explaining the overall processing of the suggestion system according to the embodiment. FIG. 1 is a diagram illustrating an overview of a first embodiment. FIG. 2 is a diagram for explaining the processing of the suggestion system in the first embodiment. 1 is a diagram for explaining an overview of a process for estimating spots visited by a user in the first embodiment; FIG. 3A to 3C are diagrams illustrating an example of elements constituting a scene and a situation in the first embodiment. FIG. 2 is a diagram showing an overview of a process for predicting a next scene from a current scene in the first embodiment. 10A to 10C are diagrams for explaining an example of a learning process for dimensionality compression processing of scene/situation data in the first embodiment. 13A to 13C are diagrams for explaining another example of the learning process for the dimensionality compression process of scene/situation data in the first embodiment. 10A to 10C are diagrams for explaining an example of a dimensionality compression process for scene/situation data in the first embodiment; 4A to 4C are diagrams illustrating an example of a process for estimating a scene and a situation using a Bayesian network according to the first embodiment; 4A to 4C are diagrams for explaining a learning process of a probabilistic model using a Bayesian network in the first embodiment. FIG. 2 is a diagram for explaining a process of estimating a probabilistic model using a Bayesian network in the first embodiment. FIG. 11 is a diagram illustrating an example of a calculation process of a suggestion score using sequence prediction according to the first embodiment. 13 is a diagram illustrating an example of suggesting travel plans and content related to travel plans, spots, or word-of-mouth information of similar users when the scene or situation is similar between the current user and similar users in the second embodiment. FIG. FIG. 11 is a diagram for explaining the processing of the suggestion system in the second embodiment. FIG. 13 is a diagram illustrating an example of a process for extracting similar users for each category in the second embodiment. 13A and 13B are diagrams showing an example of a process for calculating user similarity in the second embodiment, in which FIG. 13A shows an example of user attribute data, and FIG. 13B shows an example of a user similarity matrix. 13A and 13B are diagrams showing another example of the process of calculating user similarity in the second embodiment, in which FIG. 13A shows application operation history aggregate data, and FIG. 13B shows an example of a user similarity matrix. FIG. 11 shows another example of the process for calculating user similarity in the second embodiment, where (a) shows an example of travel plan data, (b) shows an example of content attribute aggregate data, (c) shows an example of a content attribute similarity matrix, (d) shows an example of visited area aggregate data, (e) shows an example of a visited area similarity matrix, and (f) shows an example of a user similarity matrix. 13 is a diagram for explaining the similarity of a scene/situation in the scene/situation similarity score calculation according to the second embodiment; FIG. FIG. 13 is a diagram illustrating an example of a process for calculating a similarity score of a scene/situation in the second embodiment. FIG. 13 is a diagram illustrating an example of a procedure of a suggestion score calculation process (step S308) based on similar users and similar scenes/situations according to the second embodiment. FIG. 13 is a diagram illustrating word-of-mouth suggestions according to the second embodiment. 13 is a diagram showing an example of a screen for displaying content on a user terminal device in the second embodiment. 13A and 13B are diagrams illustrating an overview of the third embodiment, in which (a) is a diagram showing an example of a screen for displaying content on a user terminal device, and (b) is a diagram showing an example of a screen for displaying a map and content on a user terminal device. 13 is a flowchart illustrating an example of a process for calculating a suggestion reason according to values and a scene/situation by the suggestion device 200 according to the third embodiment. FIG. 13 is a diagram for explaining an overview of a process for displaying a suggestion reason in the third embodiment. 13A and 13B are diagrams for explaining an example of a process for providing suggestion reasons based on a partial total of suggestion scores in the third embodiment, in which (a) is a diagram showing an example of the total of suggestion scores for each content and a partial total of each suggestion score, (b) is a diagram showing an example of a suggestion reason table, and (c) is a diagram showing an example of a suggestion screen. 13A and 13B show an example of the relationship between the search space and the value search objective function in the fourth embodiment, where (a) is a diagram for explaining that the search for values falls into a local solution and updates become less frequent, and (b) is a diagram for explaining that an optimal solution for values can be obtained by suggesting content having unexplored values. FIG. 23 is a diagram for explaining an example of an operation of a suggestion unit in the fourth embodiment. FIG. 23 is a diagram showing the relationship between a user's values, an attribute score, an article title of a content, and a ranking according to fluctuation in the fourth embodiment. FIG. 23 is a diagram showing the relationship between a user's values, attribute scores, values that temporarily increase the attribute scores, and article titles and rankings of content in the fourth embodiment. 13 is a flowchart showing an example of a process for calculating a suggestion reason according to values and a scene/situation by the suggestion device according to the fourth embodiment.

The suggestion device, suggestion method, and program to which the present invention is applied are described below with reference to the drawings. The suggestion device, suggestion method, and program to which the present invention is applied are realized, for example, by a suggestion system that suggests content to a user based on the user's values, the scene or situation in which the user is placed, etc., in response to a search request based on the user's operation. Suggestion is a process of providing the user with content including a list of content. In this way, the suggestion system realizes an information provision service that is highly convenient for the user. Note that in the following embodiment, the application field of the suggestion system is tourism and walking around town, but is not limited to these, and can be applied to any field such as shopping.

<Overall explanation of the suggestion system>
First, the overall configuration and processing contents of the suggestion system in the embodiment will be described.
FIG. 1 is a block diagram showing an example of a configuration of a suggestion system according to an embodiment. The suggestion system includes, for example, a user terminal device 100, a suggestion device 200, a content providing device 300, and an environmental information providing device 400. The user terminal device 100, the content providing device 300, and the environmental information providing device 400 are connected to, for example, a communication network. Each device connected to the communication network includes a communication interface such as a network interface card (NIC) or a wireless communication module (not shown in FIG. 1). The communication network includes, for example, the Internet, a wide area network (WAN), a local area network (LAN), and a cellular network.

The user terminal device 100 is a portable terminal device such as a smartphone or a tablet terminal. The user terminal device 100 runs a UA (User Agent) such as a browser or an application program. The UA is, for example, an application for receiving an information provision service provided by the suggestion device 200. The user terminal device 100 generates a search request including operation information based on a user's operation, location information of the user terminal device 100 using GPS (Global Positioning System, Global Positioning Satellite), and a query, and transmits the search request to the suggestion device 200 or the content providing device 300. The user terminal device 100 performs display processing, operation reception processing, and the like using information representing a list of contents provided by the suggestion device 200 and contents received from the content providing device 300.

The suggestion device 200 is, for example, an information processing device that performs processing for an information provision service. The suggestion device 200 includes, for example, a reception unit 210, a behavioral history acquisition unit 220, a scene/situation estimation unit 230, a value estimation unit 240, a search unit 250, a suggestion unit 260, and a database unit 270. The functional units such as the reception unit 210, the behavioral history acquisition unit 220, the scene/situation estimation unit 230, the value estimation unit 240, the search unit 250, and the suggestion unit 260 are realized by a processor such as a CPU (Central Processing Unit) executing a program stored in a program memory. In addition, some or all of these functional units may be realized by hardware such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field-Programmable Gate Array), or may be realized by software and hardware working together.

The reception unit 210 receives user operations and the like by acquiring operation information, which is information based on the user's operation, from the user terminal device 100. The behavior history acquisition unit 220 acquires behavior history information including operation information, user location information, content browsing history, favorite registration, and other behavior (action) history related to the user's values. The scene/situation estimation unit 230 acquires scene information representing a scene that affects the user's values and/or situation information representing the situation in which the user is placed. The value estimation unit 240 estimates the user's values in an application field such as tourism. The user's values are, for example, preferences or tastes in tourism. The user's values vary depending on the scene and/or situation, and therefore include values corresponding to the scene and/or situation. The value estimation unit 240 may estimate a persona corresponding to the user based on the user's values. The persona is a customer model that receives an information provision service. The value estimation unit 240 may also acquire, for example, fixed information of the user (address, gender, age, etc.) as user attributes. The search unit 250 searches for content based on the search request received by the reception unit 210. The suggestion unit 260 suggests content from the content searched by the search unit 250 based on the scene/situation estimated by the scene/situation estimation unit 230, the values estimated by the values estimation unit 240, etc.

The database unit 270 is realized, for example, by a hard disk drive (HDD), a flash memory, an electrically erasable programmable read only memory (EEPROM), a read only memory (ROM), or a random access memory (RAM), or a hybrid storage device that uses a combination of these. The database unit 270 may be realized by an external storage device that can be accessed via various networks. An example of an external storage device is a network attached storage (NAS) device. The database unit 270 manages information necessary for the processing of each part in the suggestion device 200.

The content providing device 300 is, for example, a server device that provides various contents in response to requests from the user terminal device 100. The content providing device 300 provides, for example, content related to restaurants, shops, public facilities (such as parks), transportation, and commercial facilities at travel destinations. The content providing device 300 stores content data of the content to be provided, including, for example, advertising information for restaurants, coupon information, and URLs for restaurants, in the content storage unit 300A. Content attribute information indicating the attributes of the content is added to the content data. The content attributes are information indicating the nature of the content, and are, for example, information corresponding to the user's values, information corresponding to the persona, and information corresponding to a scene or situation.

The environmental information providing device 400 is a server device that distributes ever-changing environmental information such as weather information, traffic information, and disaster information. The environmental information providing device 400 transmits environmental information in response to a request from the suggestion device 200, for example. The environmental information may include user information such as current location information, spot information indicating the spot of the current location (e.g., store name, tourist spot name), and electronic money balance information.

FIG. 2 is a diagram illustrating an example of the overall process of the suggestion system according to the embodiment.
First, when the user terminal device 100 installs or starts an application for receiving an information provision service, the user terminal device 100 transmits a connection request S10 to the suggestion device 200. In response to the connection request S10, the suggestion device 200 transmits content S11 for value estimation stored in the database unit 270 to the user terminal device 100. The user terminal device 100 receives a selection operation for the content S11 for value estimation and transmits operation information S12 to the suggestion device 200. The reception unit 210 receives the operation information S12 based on the selection operation, and the value estimation unit 240 estimates the user's values based on the selection operation and assigns the estimated values to the user information (step ST10).

Then, the user terminal device 100 transmits various operation information S13a, location information S13b, or scene/situation-related information S13c to the suggestion device 200. In response, the behavioral history acquisition unit 220 updates the behavioral history based on the operation information S13a, and the value estimation unit 240 updates the value based on the transmitted information (step ST11). In addition, when a condition is met, such as when a preset timing arrives or when environmental information changes, the environmental information providing device 400 transmits environmental information S14 related to the scene/situation to the suggestion device 200. In response, the value estimation unit 240 updates the value based on the transmitted environmental information S14 (step ST12).

Then, the user terminal device 100 transmits a search request S15 including a query for searching for tourism-related content to the suggestion device 200. The reception unit 210 receives the search request S15, the search unit 250 acquires content data S16 from the content providing device 300 in response to the search request S15, and the suggestion unit 260 calculates a suggestion score based on the user's current values and the scene/situation (step ST13). The suggestion unit 260 transmits to the user terminal device 100 suggestion data S17 for displaying a content list based on the calculated suggestion score. The user terminal device 100 receives the suggestion data S17 and displays the content list. When the user terminal device 100 receives an operation resulting from displaying the content list, it transmits operation information S18 to the suggestion device 200. The operation resulting from displaying the content list includes, for example, viewing content based on selecting content included in the content list, adding content to favorites, adding to a plan, and the like. The value estimation unit 240 updates the value based on the transmitted operation information S18 (step ST14).

3 is a diagram showing an example of content for value estimation in an embodiment. The content for value estimation includes a plurality of sets of images specialized for tourism and catch phrases specialized for tourism. When the user terminal device 100 receives the content for estimation, it displays a value estimation screen. The value estimation screen includes four sets of images and catch phrases corresponding to each of the images, and a question message such as "What do you like about it?" The images and catch phrases included in the content for value estimation are images and catch phrases corresponding to the attributes of the content. When the user terminal device 100 receives an operation to select one of the four sets, it transmits selection result information indicating the selected image and catch phrase to the suggestion device 200. The value estimation unit 240 estimates the user's values corresponding to the attributes of the content based on the selection result information, and the suggestion unit 260 transmits list information of the content to the user terminal device 100 based on the estimated values. As a result, the user terminal device 100 displays a recommended spot list screen including a list of contents. The user terminal device 100 may transmit filter information, such as categories and tourist spots, to the suggestion device 200, and the suggestion device 200 may transmit list information of content that matches the filter information to the user terminal device 100. The content for estimating values may include images and catchy slogans that update the values of model plans specialized for tourism.

Note that, although the value estimation screen shown in FIG. 3 displays four sets of images and catch phrases, this is not limited thereto, and eight sets may be displayed at one time. Furthermore, the sets of images and catch phrases displayed at one time may be displayed in a random order or position without being given a priority or ranking. Furthermore, it is desirable to maintain fairness between the images by making each set of images and catch phrases displayed at one time the same size and displaying them aligned on the screen so that the user can view them all at once.

Figure 4 is a diagram for explaining an example of a process for updating a user's values based on the user's actions in an embodiment. As shown in Figure 5, the value estimation unit 240 of the embodiment updates the user's values corresponding to the attributes of the content when an operation (action) is performed. When an operation such as viewing content is performed on content belonging to "local" and "traditional", the value estimation unit 240 updates the attribute scores of the user's values corresponding to "local" and "traditional" to be high. On the other hand, since no operation is performed on content belonging to "photogenic", the value estimation unit 240 updates the attribute scores of the user's values corresponding to "photogenic" to be low. In this way, the value estimation unit 240 can more accurately grasp the user's values by reflecting the user's operations in the user's values estimated using the content for value estimation.

Figure 5 is a diagram for explaining the process of updating values by the suggestion device 200 in the embodiment (step ST14). The user terminal device 100 displays the content list screen received from the suggestion device 200 (step ST20). The reception unit 210 receives operation information based on an action such as browsing content on the content list, and acquires content data on the content operated by the operation information from the content providing device 300. The suggestion unit 260 checks the content attribute (corresponding to the user's values) related to the action (step ST30). The suggestion device 200 may calculate the content attribute from information such as genre included in the content data. Next, the value estimation unit 240 calculates the user's values by combining the content attribute, the user's action, and the user's values before the update (step ST31), and updates the calculated user's values as the user's values after the update (step ST32).

Figure 6 is a flowchart showing an example of a process for making suggestions in an embodiment. First, in response to the reception unit 210 receiving a search request from the user terminal device 100, the search unit 250 acquires content data based on the search request, and the suggestion unit 260 calculates a suggestion score based on the user's values (step ST40). The suggestion unit 260 acquires operation information of the user terminal device 100 and information on the scene and/or situation from the environmental information providing device 400, acquires content data based on the scene information and/or situation information, and calculates a suggestion score (step ST41). The suggestion unit 260 increases the suggestion score for content that matches the user's values, and increases the suggestion score for content that matches the scene and/or situation in which the user is placed. The suggestion unit 260 calculates a final suggestion score by summing up the suggestion scores (step ST42). The suggestion unit 260 creates content list information so that the content with the highest suggestion score is placed at the top of the content list screen according to the final suggestion score (step S43). The suggestion unit 260 suggests content by transmitting the content list information to the user terminal device 100. This allows the suggestion device 200 to suggest a content list taking into account the user's values, scene, and/or situation.

Figure 7 is a diagram for explaining the overall processing of the suggestion system in the embodiment. In Figure 7, "UI (user)" indicates the processing of the user terminal device 100, "processing" indicates the processing of the suggestion device 200, and "data" indicates data used in the suggestion device 200 and recorded in the database unit 270. First, the user terminal device 100 accepts an operation to select content for value estimation provided by the suggestion device 200 (step S100), and the suggestion device 200 assigns values in the user attributes by content (image) diagnosis based on the user's operation (step S102), and updates the user attribute data.

Next, the user terminal device 100 requests a list of contents (step S104), and the suggestion unit 260 calculates a suggestion score using at least one of information such as user attribute data, content data, scene/situation data, user location information (user GPS data), operation information (UI operation log data), and travel plan data (step S106). The suggestion unit 260 calculates a suggestion score according to various values, scenes, and/or situations, and suggests content based on the calculated suggestion score. The suggestion unit 260 may predict the next scene or situation transitioned from the current scene or situation based on the current scene or situation, and suggest content corresponding to the predicted next scene or situation (first embodiment). Furthermore, the suggestion unit 260 may suggest content based on the behavior history of a similar user who has values similar to those of the user in a scene or situation that is the same as or similar to the scene or situation in which the user is placed, among the contents searched by the search unit 250 (second embodiment). The suggestion unit 260 may provide the user with information indicating the reason for the suggested content (third embodiment). Furthermore, the suggestion unit 260 may suggest content based on values of the user that are lower than a standard (fourth embodiment).

The suggestion unit 260 provides a list of contents to the user terminal device 100 based on the suggestion score, and the user terminal device 100 can display the list of contents and the reason for the suggested content (step S108). The user terminal device 100 accepts operations such as viewing content, adding content to favorites, or adding content to a travel plan based on a user operation (step S110). The suggestion device 200 updates UI operation log data, travel plan data, etc. based on the operation accepted by the user terminal device 100. The value estimation unit 240 may perform a process of updating values and a process of estimating a persona based on the user operation (step S112).

First Embodiment
A first embodiment of the suggestion system described above will be described below. Note that in the above embodiment, the same parts as those in the suggestion system described above are denoted by the same reference numerals.

As shown in FIG. 1, the suggestion device 200 in the first embodiment includes, for example, a reception unit 210 that receives a search request, a search unit 250 that searches for content based on the search request received by the reception unit 210, a scene/situation estimation unit 230 that estimates the scene or situation in which the user is currently placed and predicts the next scene or situation to which the current scene or situation transitions based on the estimated current scene or situation, and a suggestion unit 260 that suggests content corresponding to the next scene or situation predicted by the scene/situation estimation unit 230 from among the content searched by the search unit 250.

Figure 8 is a diagram illustrating an overview of the first embodiment. The suggestion system of the first embodiment estimates the scene or situation (including the user's behavior) in which the user is currently placed, and predicts the next scene or situation to which the current scene or situation will transition based on the estimated current scene or situation. This allows the suggestion system to suggest content related to spots that match the predicted next scene, for example. Specifically, if the current scene is "shopping at a shopping mall with friends in the afternoon," the suggestion system predicts that there is a high probability that the next scene will be "taking a break at a cafe," and can suggest content related to "taking a break at a cafe."

9 is a diagram for explaining the processing of the suggestion system in the first embodiment. In FIG. 9, "UI (user)" indicates the processing of the user terminal device 100, "processing" indicates the processing of the suggestion device 200, and "data" indicates the data used in the suggestion device 200 and recorded in the database unit 270. First, the user terminal device 100 accepts a search request requesting a list of contents (step S200), and the scene/situation estimation unit 230 estimates the spots visited by the user based on the UI operation log data and the travel plan data (step S202). The travel plan data is plan information including the location and time period of the user. Next, the scene/situation estimation unit 230 compresses the dimensionality of the scene/situation data (step S204). The scene/situation estimation unit 230 performs the dimensionality compression (step S204) using the trained model constructed by the training process (step S206).

Next, the scene/situation estimation unit 230 outputs the scene of the prediction target time (next) that is a predetermined time from the present time using a probabilistic model (step S210). The probabilistic model is a trained model constructed by the learning process (step S208). The scene/situation estimation unit 230 may, for example, predict the scene of the prediction target time based on the user's future time zone and location included in the travel plan data. Next, the suggestion unit 260 calculates a suggestion score using sequence prediction (step S212). Next, the suggestion unit 260 sums up the list of suggestion scores corresponding to the list of contents searched by the search unit 250 with the suggestion score using sequence prediction (step S214), and provides the user terminal device 100 with suggestion data for displaying a list of contents based on the summed suggestion score. As a result, the user terminal device 100 displays the list of contents (step S216). The suggestion unit 260 may provide the user terminal device 100 with information indicating the reason for the suggested content based on the probability of transitioning from the current scene or situation to the next scene or situation output from the graphical model (scene/situation estimation unit 230).

FIG. 10 is a diagram for explaining an overview of the process (step S202) of estimating the spot visited by the user in the first embodiment. The scene/situation estimation unit 230 identifies the spot visited by the user based on the location information as shown in FIG. 10(a). Next, the scene/situation estimation unit 230 estimates the scene or situation from the identified spot, the user's companion, the time period, the location, and the weather. For example, as shown in FIG. 10(b), the scene/situation estimation unit 230 can assign a scene label of "having lunch" to the scene group by extracting a group of scenes of "shopping mall", "family", and "12:00" by clustering. As shown in FIG. 10(c), when the scene/situation estimation unit 230 estimates the current scene to be "shopping", it estimates the next scene to be "taking a break" by sequence prediction processing. The sequence prediction processing may be performed, for example, using a prediction model that inputs the granularity and amount of data for identifying the current scene and outputs data for identifying the next scene. When the data output from the prediction model is "cafe," "friends," and "15:00," the scene/situation estimation unit 230 can estimate the scene "taking a break" based on the output data and suggest content for "cafe" that has a high scene attribute score for "taking a break."

Fig. 11 is a diagram showing an example of elements constituting a scene and a situation in the first embodiment, and Fig. 12 is a diagram showing an overview of the process (steps S204, S210) of predicting the next scene from the current scene in the first embodiment. As shown in Fig. 11, the scene/situation estimation unit 230 acquires scene information including information indicating behavior, companion, time period, and location, and situation information including information indicating weather and temperature. The scene/situation estimation unit 230 first acquires scene information and situation information at time t-1 by collecting scene information and situation information, classifies them into scenes at time t-1, and predicts and outputs a scene at time t from the classified scenes at time t-1 (sequential prediction of scenes). The scene/situation estimation unit 230 may predict and output the user's behavior at time t from the scene information and situation information at time t-1 (sequential prediction of behavior).

Figure 13 is a diagram for explaining an example of the learning process (step S206) for the dimensionality compression process of scene/situation data in the first embodiment. The scene/situation estimation unit 230 performs matrix decomposition on the user behavior history data shown in Figure 13(a) and obtains matrices representing the location (Where), behavior (What), location features, and behavior features, as shown in Figure 13(b). The scene/situation estimation unit 230 may generate a matrix representing the user ID and features, as shown in Figure 13(c).

Figure 14 is a diagram for explaining another example of the learning process (step S206) for the dimensionality compression process of scene/situation data in the first embodiment. The scene/situation estimation unit 230 performs dimensionality compression based on the user's behavior history (e.g., location information) to pattern the location (Where) and the behavior (What) and creates a definition of a classified scene. The scene/situation estimation unit 230 defines five classified scenes including a classification number, a location (Where), an action (What), and an interpretation of the feature amount, as shown in Figure 14, using, for example, NMF (non-negative matrix factorization) or LDA (latent Dirichlet allocation). The scene/situation estimation unit 230 inputs scene information and situation information to the trained model, and trains the parameters of the trained model so as to output one of the five classified scenes from the trained model.

Figure 15 is a diagram for explaining an example of the dimensionality reduction process (step S204) of the scene/situation data in the first embodiment. The scene/situation estimation unit 230 adopts a classified scene using a trained model trained by the learning process (step S206). Specifically, the scene/situation estimation unit 230 inputs the location (Where) and the action (What) into the trained model, calculates the score of the classified scene from the output of the trained model, and adopts the classified scene with the highest score (e.g., "Taking a leisurely break") among the multiple classified scenes. As a result, the scene/situation estimation unit 230 can dimensionally compress the location (Where) and the action (What) into one classified scene. That is, the scene/situation estimation unit 230 can estimate the time period, the accompanying person, the weather, and the classified scene as scene/situation data. Note that the scene/situation estimation unit 230 can calculate the score of the classified scene even for unknown combinations of scene/situation data by using, for example, LDA (Latent Dirichlet Allocation).

Figure 16 is a diagram showing an example of a process for estimating a scene and a situation using a Bayesian network in the first embodiment. The scene/situation estimation unit 230 as a prediction unit may input a current scene or situation into a graphical model that represents the directionality and probability of multiple scenes or situations (A, B, C), and predict the next scene or situation based on the probability of transition from the current scene or situation to the next scene or situation output from the graphical model. The scene/situation estimation unit 230 uses a Bayesian network to estimate a probability distribution for combinations of elements such as scene A transitioning to A1 or A2, scene B transitioning to B1 or B2, and scene C transitioning to C1 or C2. Similarly, the scene/situation estimation unit 230 can estimate a probability distribution for combinations of elements such as scene information and situation information.

Figure 17 is a diagram for explaining the learning process (step S208) of the probability model using a Bayesian network in the first embodiment. The scene/situation estimation unit 230 obtains the probability distribution of the classified scene at time t from the classified scene at time t-1, and constructs a learned model that obtains the probability distribution of the classified scene at time t from the probability distribution of the classified scene at time t+1. The probability distribution of the classified scene at each time varies depending on the direction of causality between each classified scene by creating a Bayesian network as described above. In the learning process, nodes that have no causal relationship (arrows in the figure) are deleted from the probability model, so that for each probability model, elements that do not contribute to the prediction of the scene or situation at the prediction target time can be found. This allows the scene/situation estimation unit 230 to predict the next scene or situation with a high probability from the current scene or situation. The trained model is not limited to the above example, and may be a trained model that calculates the probability distribution of the current classification scene from past classification scenes, and calculates the probability distribution of the current classification scene from the probability distribution of future classification scenes. For example, processing may be performed according to a probability model (trained model) that uses classification scenes before time t-2 and classification scenes after time t+2.

Figure 18 is a diagram for explaining the estimation process (step S210) of a probability model using a Bayesian network in the first embodiment. The scene/situation estimation unit 230 inputs the past scene/situation data dimensionally compressed in step S204 shown in Figure 18 (a), the future scene/situation data shown in Figure 18 (c), and the scene/situation data at the prediction target time shown in Figure 18 (b) to the learned model constructed by the learning process (step S208) of a probability model using a Bayesian network. The scene/situation estimation unit 230 selects a learned model from the time period, accompanying person, and weather, as in process (1). Next, the scene/situation estimation unit 230 outputs the probability distribution of the classified scene at the prediction target time, as shown in Figure 18 (d), based on the output of the learned model, as in process (2).

Figure 19 is a diagram showing an example of the calculation process (step S212) of the suggestion score using sequence prediction in the first embodiment. The suggestion unit 260 calculates a suggestion score according to the content attribute using the probability distribution of the classification scene estimated in step S210. For example, when a probability distribution as shown in Figure 19 (b) is output for a classification scene as shown in Figure 19 (a), the suggestion unit 260 adds 0.7 to the suggestion score of the content having the attribute "shopping/high class" because the probability of the classification number "5" is 70%. The suggestion unit 260 adds 0.05 to the suggestion score of the content having the attribute "dining/cafe" because the probability of the classification number "1" is 5%. In this way, the suggestion unit 260 performs calculations for content having an attribute corresponding to the classification scene such that the higher the probability, the higher the suggestion score is.

As described above, the suggestion system of the first embodiment can predict the next scene or situation to which the user will transition from the current scene or situation, and can suggest content that corresponds to the predicted next scene or situation from among the content searched for in response to a search request. This allows the suggestion system to predict the next action that the user is likely to take and provide suggestions.

In addition, the suggestion system of the first embodiment uses a graphical model such as a Bayesian network, making it possible to provide suggestions that allow the prediction process (reason) to be interpreted. Furthermore, the suggestion system of the first embodiment can provide the predicted next scene or situation to the user terminal device 100 as the reason for the suggestion together with a list of content, making it possible to make the user aware that content suggestions are being made according to the scene or situation, thereby improving the acceptability of the suggestion results.

Furthermore, according to the suggestion system of the first embodiment, the current scene or situation is predicted using the travel plan data, and the next scene or situation is predicted using the future location and time information contained in the user's travel plan data, so that content can be suggested taking into account information from both the past and the future.

Furthermore, according to the suggestion system of the first embodiment, the dimensionality reduction process is used to capture scenes or situations as abstract concepts such as "taking a break" or "having a drinking party," so the number of combination patterns of scenes or situations can be reduced. As a result, the suggestion system can reduce the amount of data required to make suggestions.

Second Embodiment
A second embodiment of the suggestion system described above will be described below. Note that the same reference numerals are used to designate the same parts in the above embodiment as in the above suggestion system.

As shown in FIG. 1, the suggestion device 200 in the second embodiment includes a value estimation unit 240 that estimates the user's values, a scene/situation estimation unit 230 that estimates the scene or situation in which the user is placed, a behavior history acquisition unit 220 that acquires the user's behavior history, a reception unit 210 that receives a search request from the user, a search unit 250 that searches for content based on the search request received by the reception unit 210, and a suggestion unit 260 that, when a search request is received from the user, suggests content to be provided to the user from among the content searched by the search unit 250 based on behavior history in the same or similar scene or situation as the scene or situation in which the user is placed, among the behavior history of similar users who have values similar to those of the user.

FIG. 20 is a diagram illustrating an example of suggesting travel plans and contents related to the similar user's travel plans, spots, or word-of-mouth information when the scene or situation of the user and the similar user is similar in the second embodiment. The suggestion device 200 of the second embodiment estimates similar users who have values similar to those of the user in order to suggest a list of contents to the user. The suggestion device 200 acquires the user's values, scene/situation data, travel plan data, spot information, and word-of-mouth information related to the similar users, and accumulates them in the database unit 270. When the suggestion device 200 receives a search request from the user terminal device 100 based on the user's operation, and determines that the scene/situation data of the user and the scene/situation data of the similar user are the same or similar, it suggests content included in the travel plan data created by the similar user, content related to the spot information, or content related to the word-of-mouth information.

21 is a diagram for explaining the processing of the suggestion system in the second embodiment. In FIG. 21, "UI (user)" indicates the processing of the user terminal device 100, "processing" indicates the processing of the suggestion device 200, and "data" indicates the data used in the suggestion device 200 and recorded in the database unit 270. First, the user terminal device 100 accepts a search request requesting a list of contents (step S300), and the value estimation unit 240 calculates the similarity between the user and other users by referring to at least a part of the user attribute data, UI operation log data, travel plan data, and content data viewed by the user (step S302). The reception unit 210 acquires travel plan data including the location and time period of the user, and the value estimation unit 240 may estimate the similarity between the user and similar users based on the travel plan data created by the user and the behavior history of the similar users. The value estimation unit 240 determines users whose similarity is equal to or exceeds a standard as similar users.

Next, the scene/situation estimation unit 230 refers to at least a part of the UI operation log data and the travel plan data to calculate the similarity between the scene/situation of the user and the scene/situation of the similar user (step S304). The behavior history acquisition unit 220 extracts word-of-mouth information in the similar scene/situation data by the similar user based on the similar user and the similar scene/situation data (step S306). The suggestion unit 260 calculates a suggestion score based on the similar user and the similar scene/situation data (step S308). Next, the suggestion unit 260 adds the suggestion score calculated in step S308 to the list of suggestion scores corresponding to the list of contents searched by the search unit 250 (step S312), and provides the user terminal device 100 with suggestion data for displaying a list of contents based on the suggestion score as a result of the addition. As a result, the user terminal device 100 displays the word-of-mouth information extracted in step S306 and the list of contents (step S314).

FIG. 22 is a diagram showing an example of the process (step S302) of extracting similar users for each category in the second embodiment. Since user preferences differ for each category, the value estimation unit 240 extracts similar users for each category of suggested targets. A category is, for example, information indicating a user's behavior, such as eating. When the value estimation unit 240 calculates the similarity for all categories, it extracts user B as a similar user to user A. For example, the value estimation unit 240 calculates the similarity focusing on the category "sightseeing" to extract user C as a similar user to user A.

23A and 23B are diagrams showing an example of a process (step S302) for calculating user similarity in the second embodiment, where (a) shows an example of user attribute data, and (b) shows an example of a user similarity matrix. User attribute data for multiple users is stored in the database unit 270 as shown in FIG. 23A. In the user attribute data, a value score for each category is registered for each user. The value estimation unit 240 creates a user similarity matrix as shown in FIG. 23B by calculating a correlation coefficient between users for each category in the user attribute data. The value estimation unit 240 extracts users with a high correlation coefficient from among the users registered in the database unit 270 as similar users. In this way, the value estimation unit 240 can extract similar users based on the similarity of various values of users.

24 is a diagram showing another example of the process (step S302) for calculating the similarity of users in the second embodiment, where (a) shows application operation history summary data, and (b) shows an example of a user similarity matrix. In the database unit 270, application operation history summary data for multiple users is stored as shown in FIG. 24(a). The application operation history summary data is updated by the behavior history acquisition unit 220, for example, by adding 0.2 when the user views content at a certain spot once, and by adding 1.0 when the user adds the content to a favorite or a travel plan. The value estimation unit 240 creates a user similarity matrix as shown in FIG. 24(b) by calculating a correlation coefficient between users for each category in the application operation history summary data. Since the application operation history summary data is sparse data with many dimensions, the correlation coefficient may be calculated after dimensional compression using, for example, NMF (non-negative matrix factorization) or LDA (latent Dirichlet allocation). The value estimation unit 240 extracts users with high correlation coefficients from among the users registered in the database unit 270 as similar users. In this way, the value estimation unit 240 can extract similar users based on the user's operation history.

25 is a diagram showing another example of the process (step S302) for calculating the similarity of users in the second embodiment, where (a) shows an example of travel plan data, (b) shows an example of content attribute aggregate data, (c) shows an example of a content attribute similarity matrix, (d) shows an example of visited area aggregate data, (e) shows an example of a visited area similarity matrix, and (f) shows an example of a user similarity matrix. As shown in FIG. 25(a), the database unit 270 stores travel plan data including time periods and places (spots) for multiple users. The value estimation unit 240 creates the content attribute aggregate data shown in FIG. 25(b) by aggregating the average of the content attributes of the spots included in the travel plan shown in FIG. 25(a). The content attribute aggregate data includes the average value of the attributes of the content related to the places in the travel plan for each user. Next, the value estimation unit 240 creates the content attribute similarity matrix shown in FIG. 25(c) by calculating the cosine similarity between users. The content attribute similarity matrix includes the similarity between users. The value estimation unit 240 creates the visited area summary data shown in FIG. 25(d) by attaching a flag to the cities, towns, and villages included in the travel plan shown in FIG. 25(a). The visited area summary data includes the visited areas with flags attached for each user. Next, the value estimation unit 240 creates the visited area similarity matrix shown in FIG. 25(e) by calculating the cosine similarity between the users. The visited area similarity matrix includes the similarity between the users. The value estimation unit 240 creates the user attribute similarity matrix shown in FIG. 25(f) by calculating the geometric mean of the content attribute similarity matrix and the visited area similarity. The value estimation unit 240 extracts users with a high correlation coefficient from among the users registered in the database unit 270 as similar users. In this way, the value estimation unit 240 can extract similar users based on the similarity of the content attributes and visited areas in the travel plan.

Figure 26 is a diagram for explaining the scene/situation similarity in the scene/situation similarity score calculation (step S304) of the second embodiment. The scene/situation estimation unit 230 calculates the scene/situation similarity by performing processing such as a similarity measurement method on the scene/situation items and values. If the scenes/situations of the user and similar users match in "accompanying person" and the value matches in "with friend", the scene/situation estimation unit 230 calculates the similarity as "1", and if they are different, calculates the similarity as "0". In this way, the scene/situation estimation unit 230 can register information in the database unit 270 that associates preset scene/situation items, values, and similarity measurement methods, and calculate the scene/situation similarity of the user and similar users according to the registered information.

27 is a diagram showing an example of the process (step S304) for calculating the similarity score of the scene/situation in the second embodiment. The scene/situation estimation unit 230 compares the scene/situation of the target user to be suggested with the scene/situation of similar users, and calculates the similarity score of the scene/situation for each content related to the visited destination. The scene/situation estimation unit 230 calculates a weighted average, for example, as shown in the following formula.
(Scene-situation similarity score) = Σ((individual similarity) × (weight))/Σ(weight)
As shown in Fig. 27(a), when the user is searching for a place to visit, it is assumed that the similar user's visit destination is "XX Cafe." In this case, the scene/situation estimation unit 230 calculates an individual similarity for each scene/situation as shown in Fig. 27(b) by comparing the scene/situation of the user shown in Fig. 27(a) with the scene/situation of the similar user. The scene/situation estimation unit 230 multiplies the individual similarity by a weight for each scene/situation that is set in advance, and calculates a scene/situation similarity score by summing up the multiplied values of all similarities, as shown below.
(Scene-situation similarity score) = (1.0 x 1.0 + 0.5 x 1.0 + 0.9 x 1.0 + 1.0 x 0.2 + 1.0 x 0.3 + 0.0 x 0.3 + 0.0 x 0.8 + 0.0 x 0.5 + 0.6 x 0.5 + 1.0 x 0.5) / (1.0 + 1.0 + 1.0 + 0.2 + 0.3 + 0.3 + 0.8 + 0.5 + 0.5 + 0.5) = 0.606

Figure 28 is a diagram showing an example of the processing procedure of the suggestion score calculation process (step S308) based on similar users and similar scenes/situations in the second embodiment. As shown in Figure 28 (a), for example, when user A wants to search for a dining spot, the similarities of similar users B and C to user A in the dining category are 0.7 and 0.1, respectively. Note that the similarities of similar users B and C to user A in the sightseeing category as shown in Figure 28 (c) are not used in the calculation of the suggestion score. Also, as shown in Figure 28 (d), the scene/situation similarities for each of similar user B's visited destinations (○○ cafe, △△ restaurant) are 0.6 and 0.4, and the scene/situation similarities for each of similar user C's visited destinations (○○ cafe, □□ izakaya) are 0.7 and 0.2. In this case, the suggestion unit 260 calculates a value (0.42) by multiplying the similarity score of similar user B (0.7) by the scene/situation similarity (0.6) of similar user B's ○○ Cafe, a value (0.28) by multiplying the similarity score of similar user B (0.7) by the scene/situation similarity (0.4) of similar user B's △△ Restaurant, a value (0.07) by multiplying the similarity score of similar user C (0.1) by the scene/situation similarity (0.7) of similar user C's ○○ Cafe, and a value (0.02) by multiplying the similarity score of similar user C (0.1) by the scene/situation similarity (0.2) of similar user C's □□ Izakaya. The suggestion unit 260 adds the value (0.42) obtained by multiplying the similarity score (0.7) of similar user B by the scene/situation similarity (0.6) of similar user B's XX cafe to the suggestion score of XX cafe, and adds the value (0.28) obtained by multiplying the similarity score (0.7) of similar user B by the scene/situation similarity (0.4) of similar user B's △△ diner to the suggestion score of △△ diner. The suggestion unit 260 does not add the value (0.07) obtained by multiplying the similarity score (0.1) of similar user C by the scene/situation similarity (0.7) of similar user C's XX cafe to the suggestion score of XX cafe, but adds the value (0.02) obtained by multiplying the similarity score (0.1) of similar user C by the scene/situation similarity (0.2) of similar user C's □□ izakaya to the suggestion score of □□ izakaya. This allows the suggestion unit 260 to calculate a suggestion score based on the similarity scores of similar users and the similarity scores of similar scenes/situations.

Figure 29 is a diagram illustrating word-of-mouth suggestions (step S306) in the second embodiment. The suggestion unit 260 may suggest word-of-mouth information narrowed down by similar users, similar scenes/situations, and user ratings from among the word-of-mouth information stored in the database unit 270. For example, when a list of content is requested by a user, the suggestion unit 260 first extracts word-of-mouth information posted by similar users, then narrows down the extracted word-of-mouth information to word-of-mouth information of similar scenes/situations, and further narrows down the word-of-mouth information narrowed down by similar scenes/situations to highly rated word-of-mouth information.

Figure 30 is an example of a screen that displays content on the user terminal device 100 in the second embodiment. As shown in Figure 30, the suggestion unit 260 can suggest content based on a suggestion score calculated from similar users and similar scenes/situations, and can also suggest word-of-mouth information narrowed down by similar users, similar scenes/situations, and evaluations.

As described above, according to the suggestion system of the second embodiment, content to be provided to the user is suggested based on the behavioral history of similar users who have values similar to those of the user, in scenes or situations that are the same or similar to the scene or situation in which the user is placed, so that the user's satisfaction with the suggested content can be improved. Furthermore, according to the suggestion system, suggestions are made in combination with user-based collaborative filtering, so that the suggested content is more likely to be unexpected, and the user is more likely to make new discoveries. Furthermore, according to the suggestion system, the fact that the scene or situation of the user and the scene or situation of the similar user are the same or similar can be reflected in the suggestion score, so that user-based collaborative filtering suggestions according to the scene or situation can be made.

In addition, according to the suggestion system of the third embodiment, content is suggested using low-noise data, such as travel plan data created by the user, so that content that matches the preferences of the user can be suggested. Furthermore, according to the suggestion system of the third embodiment, word-of-mouth information of similar users related to the suggested content is provided, so that a third-party opinion can be provided to the user, which ultimately improves the persuasiveness of the suggested content.

Third Embodiment
A third embodiment of the suggestion system described above will be described below. Note that the same reference numerals are used to designate the same parts in the above embodiment as in the above suggestion system.

As shown in FIG. 1, the suggestion device 200 in the third embodiment includes a value estimation unit 240 that estimates the user's values, a scene situation estimation unit 230 that estimates the scene or situation in which the user is placed, a reception unit 210 that receives a search request from the user, a search unit 250 that searches for content based on the search request received by the reception unit 210, and a suggestion unit 260 that, when a search request is received from the user, suggests content based on a score obtained by adding up the score total of content based on the user's values and the score total of content based on the scene or situation in which the user is placed, among the content searched by the search unit 250. The suggestion unit 260 provides the user with information indicating the reason for the suggested content based on the score total.

31 is a diagram for explaining an overview of the third embodiment, where (a) is a diagram showing an example of a screen displaying content on the user terminal device 100, and (b) is a diagram showing an example of a screen displaying a map and content on the user terminal device 100. For example, when the suggestion device 200 suggests a list of content, the suggestion device 200 transmits information showing the list of content and information showing the reason for the suggested content to the user terminal device 100. As shown in FIG. 31(a), the user terminal device 100 can display, for example, a list of content including the content of XX Cafe, and display text images such as "With a partner" and "Recommended for photogenic lovers" as the reason for the suggestion for the XX Cafe content. As shown in FIG. 31(b), the user terminal device 100 can display the cafe content and the reason for the suggestion of the content redundantly on the map.

Figure 32 is a flowchart showing an example of the process of calculating a suggestion reason according to values and a scene/situation by the suggestion device 200 in the third embodiment. In Figure 32, "UI (user)" indicates the process of the user terminal device 100, "processing" indicates the process of the suggestion device 200, and "data" indicates data used by the suggestion device 200 and recorded in the database unit 270. First, the user terminal device 100 accepts a search request requesting a list of contents (step S400) and transmits the search request to the suggestion device 200. The search unit 250 searches for contents that match the search request and obtains a list of contents based on the search request.

The suggestion unit 260 refers to the user attribute data and at least a portion of the content data for each piece of content included in the list of content acquired by the search unit 250, and calculates a suggested score total based on the various values estimated by the value estimation unit 240 (step S402). Next, the suggestion unit 260 refers to the UI operation log data, the scene/situation rule engine, and at least a portion of the content data for each piece of content included in the list of content acquired by the search unit 250, and calculates a suggested score total based on the combination of scenes and situations estimated by the scene/situation estimation unit 230 (step S404). Next, the suggestion unit 260 refers to the user attribute data and at least a portion of the content data for each piece of content included in the list of content acquired by the search unit 250, and calculates a suggested score total for each persona corresponding to the user (step S406).

Next, the suggestion unit 260 calculates the sum of the suggestion score totals calculated in steps S402, S404, and S406 for each piece of content included in the list of content acquired by the search unit 250 (step S408). At this time, the suggestion unit 260 may calculate the sum of the suggestion scores by taking into account the suggestion scores using sequence prediction described in step S212 of FIG. 9 as the score total, and/or the suggestion scores based on similar users and similar scenes/situations described in step S308 of FIG. 21 as the score total.

The suggestion unit 260 then provides the user terminal device 100 with suggestion data for displaying a list of content based on the totalized suggestion score. The suggestion unit 260 also creates a suggestion reason based on the totalized suggestion score, and provides the user terminal device 100 with data for displaying the suggestion reason for the content included in the list of content. As a result, the user terminal device 100 displays the list of content and the suggestion reason (step S412).

In the process of displaying the suggested reasons described above, the suggestion device 200 displays candidate suggested reasons for each piece of content included in the list of content in order of priority. The suggestion device 200 determines the priority of the suggested reasons based on the score total of the suggested reasons. When displaying a piece of content included in the list of suggested content, the user terminal device 100 controls the number of suggested reasons corresponding to that piece of content to be displayed.

Figure 33 is a diagram for explaining an overview of the process of displaying the suggestion reason in the third embodiment. It is desirable for the suggestion device 200 to provide the user with, for example, the suggestion reason that the content captures a scene/situation, that the content captures a variety of values, or that the content captures a persona. For example, when making a suggestion that captures a scene/situation (layer), the suggestion device 200 may provide the user with suggestion reasons such as "It's sunny and the sunset is beautiful" or "The scenery is nice" when a rule such as "Suggest a sunset on a fine day" set in the scene/situation rule engine is activated (item, summary). Furthermore, for example, when making a suggestion that captures a variety of values (layer), if there is content that is highly matched to the user's values, the suggestion device 200 may provide the user with suggestion reasons such as "Recommended for those who like high class".

The suggestion device 200 may provide the suggestion reasons in a format that allows the user terminal device 100 to change the display order of the suggestion reasons. The suggestion device 200 may provide the user terminal device 100 with data in which sets of items and phrases expressing the suggestion reasons, such as those shown in FIG. 33, are arranged in order of priority, and the user terminal device 100 may select items for the suggested content and display the suggestion reasons corresponding to the items. Furthermore, the suggestion device 200 may provide the user terminal device 100 with the suggestion reasons for items with score totals higher than the standard in order of priority. For example, when the score totals are A>B>C>D>E, the suggestion device 200 may:
{[“It's sunny and the sunset is beautiful”, Score_minutes_total_A, Scene_status_rule],
[“Within 300m”, Score Minute B, Scene Status_Distance],
[“Recommended for high class lovers”, Score C, Diverse values_values],
[“With friends”, score total D, scene situation_companion],
[“Ocean View”, Score E, Diverse Values_Details]}
As such, information including the suggestion reason, score total, and item may be provided.
For example, the suggestion device 200 considers various values, companions, and persona rules on the information collection screen, and therefore, when the score total is F>G>H,
{[“Recommended for those who like high class”, score total F, diverse values_values],
[“With friends”, score total G, scene situation_companion],
[“Ocean View”, Score H, Diverse Values_Detailed Information]}
As such, information including elements that correspond to various values, companions, and persona rules and contribute to the suggestions may be provided.

Figure 34 is a diagram for explaining an example of a process for providing a suggestion reason based on a subtotal of the suggestion score in the third embodiment, where (a) is a diagram showing an example of the total of the suggestion scores for each content and the subtotal of each suggestion score, (b) is a diagram showing an example of a suggestion reason table, and (c) is a diagram showing an example of a suggestion screen. For example, as shown in Figure 34 (a), the suggestion score for the value corresponding to one content included in the list of contents is 2.2, the suggestion score for the scene/situation is 1.5, the suggestion score for the sequence prediction is 0.7, the suggestion score for similar users and similar scenes/situations is 0.4, and the total of the suggestion scores is 4.8. Each suggestion score includes multiple score subtotals. The suggestion unit 260 creates a suggestion reason table in which the score types and suggestion reasons are arranged in descending order of the score subtotals, as shown in Figure 34 (b). The suggestion unit 260 transmits the suggestion reasons to the user terminal device 100 so that the top two suggestion reasons among the score totals included in the suggestion reason table are displayed. This allows the user terminal device 100 to display the two suggestion reasons together with the content of XX Cafe, as shown in FIG. 34(c).

The suggestion device 200 may provide a suggestion reason that is not related to the score calculation. For example, the suggestion device 200 may display a suggestion reason such as "Many users are interested in" for a highly popular piece of content in the list of contents.

As described above, according to the suggestion device 200 of the third embodiment, when a search request is received, among the contents searched by the search unit 250, content is suggested based on a score obtained by adding up the score total of the content based on the user's values and the score total of the content based on the scene or situation in which the user is placed, and information indicating the reason for the suggested content based on the score total can be provided to the user. According to the suggestion system of the third embodiment, the values, scenes, or situations that have a large contribution rate (= score total) to the suggestion score can be displayed on the user terminal device 100 together with a list of contents as the suggestion reason. As a result, according to the suggestion system of the third embodiment, the user can recognize that content that matches the values, scene, or situation is being suggested, and the acceptability of the suggestion result can be improved.

<Fourth embodiment>
A fourth embodiment of the suggestion system described above will be described below. Note that the same reference numerals are used to designate the same parts in the above embodiment as in the above suggestion system.

As shown in FIG. 1, the suggestion device 200 in the fourth embodiment includes a reception unit 210 that receives a search request from a user, a search unit 250 that searches for content based on the search request received by the reception unit 210, a behavior history acquisition unit 220 that acquires the user's behavior history, a value estimation unit 240 that updates attribute scores corresponding to multiple user values based on the behavior history acquired by the behavior history acquisition unit 220 and estimates the user's values based on the attribute scores, and a suggestion unit 260 that suggests content that matches the user's values based on the attribute scores of the user's values from among the content searched by the search unit 250, and the suggestion unit 260 temporarily changes attribute scores of the multiple user values that are lower than a standard to higher values to suggest content.

The suggestion device 200 in the above-described embodiment attributes the user's values to capture the user's preferences. In order to correctly capture the user's values, the value estimation unit 240 performs processing to estimate and update the user's values and persona based on the attributes of the content when it acquires the user's actions such as viewing content, adding to favorites, and adding to travel plans. By updating the user's values, the suggestion device 200 preferentially suggests content that matches the user's preferences.

However, there are times when a certain value is difficult to update in the suggestion device 200. For example, if a user who has a high preference for photogenic and local (local climate) frequently browses content with photogenic values, the suggestion device 200 will preferentially suggest photogenic content. In this state, even if the user has a high preference for local, local content may not be suggested, and the user may not realize that the local content matches his or her preferences. In order to solve this problem, it is desirable to suggest content so as to search the entire set of user values. Therefore, the suggestion unit 260 of the fourth embodiment has a function of suggesting content that matches the user's value based on the attribute score of the user's value among the content searched by the search unit 250, and a function of suggesting content by temporarily changing the attribute scores of multiple user values that are lower than the standard to higher values. This allows the suggestion unit 260 to provide content having values (values that have not been explored) that have had their attribute scores updated less frequently among a plurality of user values, and when the user discovers the content and acts on it, the values that have not been explored can be updated. In other words, the suggestion unit 260 can make serendipity suggestions that are both useful and unexpected by suggesting content as if exploring the values of the user across all of the preset values.

Figure 35 is a diagram showing an example of the relationship between the search space and the value search objective function in the fourth embodiment, where (a) is a diagram showing a state where the search for values has progressed, and (b) is a diagram showing a state when content for which the value search has not progressed is suggested. The vertical axis in Figure 35 is the value search objective function, and the horizontal axis is the search space. The value search objective function is a function whose value becomes lower as the search for values set in the suggestion system progresses, and the search space is a space representing the values held by the user. As shown in Figure 35 (a), when content having a specific value is provided multiple times, the user's value for the specific value is updated multiple times, so that even if further content having the specific value is provided, the degree of update of the user's value becomes gentle, and the value objective function for the search space is in a state in which a local solution is obtained. However, it is desirable to provide content having values other than the specific value and capture the original values held by the user, thereby bringing the value for the search space closer to the optimal solution. In response to this, the suggestion unit 260 provides content that has values corresponding to the user's specific values and that has been provided less frequently (the user's values have not been updated much), thereby bringing the values for the search space closer to the optimal solution, as shown in FIG. 35(b).

Figure 36 is a diagram for explaining an example of the operation of the suggestion unit 260 in the fourth embodiment. For example, if the attribute score of the user value of photogenic for user A is high and the attribute score of the user value of local is low, content with the value of photogenic is frequently suggested, but content with the value of local is unlikely to be suggested. In response to this, the suggestion unit 260 temporarily increases the attribute score of the user value of local, thereby making it easier for the content with the local value to be temporarily suggested. That is, the suggestion unit 260 changes the value with a low attribute score to "the same value as the value with the maximum value" by fluctuating the attribute score of the user value, and calculates the suggestion score. The suggestion unit 260 may select one of the attribute scores of the user value to be changed in a round robin manner from values other than the attribute score of the user value with the maximum value. In this way, the suggestion unit 260 can accurately grasp the user's values by including in the list of contents content for which the exploration of the user's values may not have progressed.

Figure 37 is a diagram showing the relationship between the user's values, attribute scores, article names of content, and rankings according to fluctuations in the fourth embodiment. When the suggestion unit 260 does not fluctuate the attribute scores of the user's values, a list of article names (contents) (1st to 12th) having the value "looks good" as an attribute, which has a high attribute score for user A, is suggested. For example, when the suggestion unit 260 increases the attribute score of the value "learning" by fluctuating the value, it can vary the rankings included in the article name list when the attribute scores of the user's values are not fluctuated. Note that the suggestion unit 260 may, for example, not fluctuate an article in a specific ranking (6th place) in the list of contents, but fluctuate the ranking between 1st and 5th place according to the fluctuations, and fluctuate the ranking between 7th and 12th place according to the fluctuations.

Figure 38 is a diagram showing the relationship between a user's values, attribute scores, values whose attribute scores have been temporarily increased, and the article titles and rankings of content in the fourth embodiment. For example, when the attribute score of the value "classic" is the highest among the attribute scores of user A's values as shown in Figure 38 (a), the suggestion unit 260 suggests a list of content from 1st to 10th place in "classic" as shown in Figure 38 (b). For example, when the attribute score of the value "local" is increased, the suggestion unit 260 can suggest the 13th content between 7th and 8th place when the attribute score of the value "classic" is the highest, as shown for "local" in Figure 38 (b).

Figure 39 is a flowchart showing an example of a process for calculating a suggestion reason according to values and a scene/situation by the suggestion device 200 in the fourth embodiment. In Figure 39, "UI (user)" indicates the process of the user terminal device 100, "processing" indicates the process of the suggestion device 200, and "data" indicates data used in the suggestion device 200 and recorded in the database unit 270. First, the user terminal device 100 accepts a search request requesting a list of contents (step S500) and transmits the search request to the suggestion device 200. The search unit 250 searches for contents matching the search request and obtains a list of contents based on the search request. As in the above-mentioned embodiment, the suggestion unit 260 can suggest contents matching the user's values based on the attribute score of the user's values from among the contents searched by the search unit 250. That is, the higher the attribute score of the user's values is, such as the maximum value or the average value, the more frequently the suggestion unit 260 can suggest contents.

In the fourth embodiment, the suggestion unit 260 refers to the user attribute data and temporarily raises the attribute score of at least one of the user's values (step S502). At this time, the suggestion unit 260 may temporarily raise the attribute score of a user value whose attribute score has been updated less frequently among the multiple user values. The suggestion unit 260 may also temporarily raise the attribute score of a user value whose attribute score has been updated less frequently among the multiple user values.

Next, the suggestion unit 260 refers to at least a portion of the content data for each piece of content included in the list of content acquired by the search unit 250, and calculates a total suggestion score based on the various values estimated by the value estimation unit 240 (step S504). Next, the suggestion unit 260 adds up the suggestion scores calculated in step S504 to the list of suggestion scores corresponding to the list of content searched by the search unit 250 (step S506), and provides the user terminal device 100 with suggestion data for displaying a list of content based on the suggestion scores obtained as a result of the addition. As a result, the user terminal device 100 displays the list of content (step S508).

As described above, according to the suggestion device 200 of the fourth embodiment, attribute scores corresponding to each of a plurality of user values are updated based on the behavioral history acquired by the behavioral history acquisition unit 220, the user's values are estimated based on the attribute scores, content that matches the user's values is suggested based on the attribute scores of the user's values, and attribute scores that are lower than a reference among the attribute scores of the plurality of user values are temporarily changed to higher values to suggest content. As a result, according to the suggestion device 200, content that may not have been explored in the user's values is suggested at the top, so suggestions can be made according to the values that the user potentially prefers.

Note that although each embodiment and each modified example have been described, these are merely examples and are not intended to be limiting. For example, any of the embodiments or modified examples, or a part of each embodiment or a part of each modified example, may be combined with one or more other embodiments or one or more other modified examples to realize one aspect of the present invention.

In addition, the programs for executing the processes of the user terminal device 100, the suggestion device 200, the content providing device 300, and the environmental information providing device 400 in this embodiment may be recorded on a computer-readable recording medium, and the programs recorded on the recording medium may be read into a computer system and executed to perform the various processes described above related to the user terminal device 100, the suggestion device 200, the content providing device 300, and the environmental information providing device 400.

Note that the term "computer system" here may include hardware such as the OS and peripheral devices. In addition, if a WWW system is used, the term "computer system" also includes the homepage provision environment (or display environment). Furthermore, "computer-readable recording media" refers to storage devices such as flexible disks, magneto-optical disks, ROMs, writable non-volatile memories such as flash memory, portable media such as CD-ROMs, and hard disks built into computer systems.

Furthermore, the term "computer-readable recording medium" refers to a volatile memory (e.g., DRAM (Dynamic Random Access Memory)) within a computer system that is a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line.
This also includes those that hold a program for a certain period of time, such as a random access memory (RDMA). The program may be transmitted from a computer system in which the program is stored in a storage device to another computer system via a transmission medium, or by a transmission wave in the transmission medium.

Here, the "transmission medium" that transmits the program refers to a medium that has the function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The above program may also be one that realizes part of the above-mentioned functions. Furthermore, it may be one that can realize the above-mentioned functions in combination with a program already recorded in the computer system, a so-called differential file (differential program).

The above describes an embodiment of the present invention in detail with reference to the drawings, but the specific configuration is not limited to this embodiment, and includes designs that do not deviate from the gist of the invention.

REFERENCE SIGNS LIST 100 User terminal device 200 Suggestion device 210 Reception unit 220 Action history acquisition unit 230 Scene/situation estimation unit 240 Value estimation unit 250 Search unit 260 Suggestion unit 270 Database unit 300 Content providing device 300A Content storage unit 400 Environmental information providing device

Claims (3)

an estimation content providing unit that provides a plurality of estimation content including images and catch phrases specialized in an application field, which is one field in which the suggestion device realizes an information provision service;
an operation receiving unit that receives a user's operation to select one of the plurality of estimation contents;
a value estimation unit that estimates values of a plurality of users in the application field based on the operation received by the operation reception unit;
a scene situation estimation unit that estimates a scene or situation from a spot identified from the user's position information and information including the user's companion, time period, and weather;
A reception unit that receives a search request from a user;
a search unit that searches for content based on the search request received by the reception unit;
a suggestion unit which calculates a higher score for content based on each of the user's multiple values for a content that has a content attribute that matches the user's values, calculates a higher score for content based on a scene or situation for a content that has a content attribute that matches a scene or situation estimated for the user, and transmits to a user terminal device a content including a list of content that has a high score obtained by adding up the score for content based on each of the user's multiple values and the score for content based on a scene or situation in which the user is placed among the content searched for by the search unit,
The suggestion unit creates a reason for the inclusion of each piece of content in the content list based on a score summary, and transmits information indicating the reason to the user terminal device of the user. Providing a plurality of estimation contents including images and catch phrases specialized in an application field, which is one field in which the suggestion device realizes an information provision service;
receiving a user operation to select one of the plurality of estimation contents;
estimating values of a plurality of users in the application field based on the received operation;
A step of estimating a scene or situation in which the user is placed based on information including a spot identified from the user's location information, the user's companion, a time period, and weather;
receiving a search request from a user;
searching for content based on the search request;
a step of calculating a higher score total for content based on each of the user's multiple values for a content that has a content attribute that matches the user's values, calculating a higher score total for content based on a scene or situation for a content that has a content attribute that matches the scene or situation estimated for the user, and transmitting to a user terminal device content including a list of content that has a high score among the searched contents, calculated by adding up the score total for content based on each of the user's multiple values and the score total for content based on the scene or situation in which the user is placed;
generating a reason for the inclusion of each piece of content in the list of contents based on the score distribution, and transmitting information indicating the reason to a user terminal device of the user;
Suggestion methods, including: The computer of the information processing device
Providing a plurality of estimation contents including images and catch phrases specialized in an application field, which is one field in which the suggestion device realizes an information provision service;
receiving a user operation to select one of the plurality of estimation contents;
estimating values of a plurality of users in the application field based on the received operation;
A step of estimating a scene or situation in which the user is placed based on information including a spot identified from the user's location information, the user's companion, a time period, and weather;
receiving a search request from a user;
searching for content based on the search request;
a step of calculating a higher score total for content based on each of the user's multiple values for a content that has a content attribute that matches the user's values, calculating a higher score total for content based on a scene or situation for a content that has a content attribute that matches the scene or situation estimated for the user, and transmitting to a user terminal device content including a list of content that has a high score among the searched contents, calculated by adding up the score total for content based on each of the user's multiple values and the score total for content based on the scene or situation in which the user is placed;
generating a reason for the inclusion of each piece of content in the list of contents based on the score distribution, and transmitting information indicating the reason to a user terminal device of the user;
A program to execute.

Images