Recommendation System

Abstract

The objective of the present invention is to more appropriately learn a method for determining a recommendation expression.　A recommendation system 10 comprises: a user information acquiring unit 11 for acquiring user information relating to a user to be provided with a recommendation; a content determining unit 12 for determining content to be recommended to the user, on the basis of the user information; an expression determining unit 13 for determining an expression to be used when recommending the determined content to the user, on the basis of the user information; a difficulty level information acquiring unit 14 for acquiring difficulty level information indicating a difficulty level with which the user behaves with respect to the determined content; a behavior information acquiring unit 15 for acquiring behavior information indicating a behavior of the user with respect to the recommendation made to the user; and a learning unit 16 for learning the method for determining the recommendation expression, taking into account the difficulty level of the behavior of the user to whom the recommendation is made, on the basis of the difficulty level information and the behavior information.

Description

[Technical Field]

[0001] The present invention relates to a recommendation system. [Background technology]

[0002] Patent Document 1 describes a system that recommends content according to a customer's preferences by reflecting the content on a screen designed according to the customer's preferences. This system uses a learning-type artificial intelligence to generate content information, which is information about content to be recommended to a customer, and template information, which is information about a template including an area for displaying information included in the content information, based on customer attribute information indicating the customer's attributes and customer behavior history information indicating the customer's behavior history. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-61525 Summary of the Invention [Problem to be solved by the invention]

[0004] As shown in Patent Document 1, the effectiveness of a recommendation can be increased by determining the expression of the recommendation (the design of the recommended screen in Patent Document 1) when making a recommendation. In particular, the effectiveness of a recommendation can be further increased by considering the psychological bias of the user who is the target of the recommendation when making the expression of the recommendation. By learning a method for determining the expression of a recommendation based on the user's reaction to the recommendation, the expression of the recommendation can be determined more appropriately.

[0005] However, a user's reaction to a recommendation depends not only on the wording of the recommendation but also on the recommended item. Therefore, simply using the user's reaction may not necessarily result in appropriate learning. For example, if content that matches the user's tastes is recommended, the user may use the content regardless of the wording of the recommendation.

[0006] One embodiment of the present invention has been made in consideration of the above, and aims to provide a recommendation system that can more appropriately learn how to determine the representation of recommendations. [Means for solving the problem]

[0007] In order to achieve the above object, a recommendation system according to one embodiment of the present invention comprises: a user information acquisition unit that acquires user information related to a user who is the target of the recommendation; a content determination unit that determines content to recommend to the user based on at least a portion of the user information acquired by the user information acquisition unit; an expression determination unit that determines an expression to use when recommending the content determined by the content determination unit to the user based on at least a portion of the user information acquired by the user information acquisition unit; a difficulty information acquisition unit that acquires difficulty information indicating the difficulty with which the user will act on the content determined by the content determination unit; a behavioral information acquisition unit that acquires behavioral information indicating the user's behavior in response to the recommendation made to the user in accordance with the decisions made by the content determination unit and the expression determination unit; and a learning unit that learns the determination method of the expression determination unit based on the difficulty information acquired by the difficulty information acquisition unit and the behavioral information acquired by the behavioral information acquisition unit, taking into account the difficulty with which the user who is the target of the recommendation will act.

[0008] In a recommendation system according to an embodiment of the present invention, when learning a method for determining how to express recommendations, the difficulty level at which a user will act on recommended content is taken into consideration. Therefore, for example, it is possible to learn a method for determining how to express recommendations by excluding the influence of the user's interests and preferences from the user's actions regarding recommendations. As a result, the recommendation system according to an embodiment of the present invention can more appropriately learn a method for determining how to express recommendations. [Effects of the Invention]

[0009] According to one embodiment of the present invention, it is possible to more appropriately learn a method for determining the representation of a recommendation. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating a configuration of a recommendation system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a diagram schematically illustrating an overview of recommendations. [Figure 3] 10 is a table showing an example of user information used to determine a store to be recommended to a user. [Figure 4] 10 is a table showing an example of information used to determine a store to be recommended to a user. [Figure 5] 10 is a table showing an example of information used to determine the content of a recommendation to a user. [Figure 6] 10 is a table showing an example of information used to determine how to express a recommendation to a user. [Figure 7] FIG. 10 is a diagram illustrating an example of calculation of an evaluation value using a psychological bias estimation model. [Figure 8] 10 is a table showing an example of an evaluation value calculated for each psychological bias. [Figure 9] 10 is a table showing an example of information used to determine how to express a recommendation to a user. [Figure 10] 10 is a table showing an example of information used to train a psychological bias estimation model. [Figure 11] FIG. 10 is a diagram illustrating an example of learning a psychological bias estimation model. [Figure 12] 1 is a flowchart showing processing executed in a recommendation system according to an embodiment of the present invention. [Figure 13] FIG. 1 is a diagram illustrating a hardware configuration of a recommendation system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of a recommendation system according to the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same elements are designated by the same reference numerals, and duplicated explanations will be omitted.

[0012] FIG. 1 shows a recommendation system 10 according to this embodiment. The recommendation system 10 is a system (device) that recommends content to a user. In this embodiment, the content recommended to a user is a restaurant. The content recommended to a user may be any item that can be a recommendation target. For example, the content recommended to a user may be a product from an online store or an EC (electronic commerce) site.

[0013] The recommendation system 10 makes recommendations by, for example, transmitting information related to the recommendations to a terminal 20 used by a user. The terminal 20 is a device that can transmit and receive information to and from the recommendation system 10 via a network such as a mobile communication network, and can also process information related to the recommendations. The terminal 20 is a device such as a mobile phone, a smartphone, or a PC (personal computer). The transmission and reception of information related to recommendations between the terminal 20 and the recommendation system 10, as well as the input and output of the information, may be performed by a dedicated application installed on the terminal 20. A portion of the information used in the recommendation system 10 may be acquired by the terminal 20 and transmitted to the recommendation system 10.

[0014] The recommendation system 10 is configured by a computer such as a PC (personal computer) or a server device having a communication function. The recommendation system 10 may be configured by a plurality of computers. The recommendation system 10 can transmit and receive information to and from terminals 20 via a network such as a mobile communication network.

[0015] The recommendation system 10 determines the content (restaurants) to be recommended for each user. The recommendation system 10 also determines the wording of the recommendation for each recommendation and makes the recommendation using the determined wording. The recommendation wording is, for example, a nudge wording that is a wording used when recommending content. The recommendation wording is also based on the user's psychological bias (hereinafter referred to as psychological bias) (cognitive bias). Psychological bias is a psychological tendency that a user has when deciding on an action to take in response to a recommendation. Examples of psychological biases include "loss aversion," which is the desire to avoid losses, and "conformity," which is the desire to conform to others.

[0016] As shown in FIG. 2, for example, the recommendation system 10 stores a content selection model in advance and determines content to be recommended based on the content selection model. The recommendation system 10 also stores a psychological bias estimation model in advance, estimates a psychological bias based on the psychological bias estimation model, and determines an expression according to the estimated psychological bias. The recommendation system 10 recommends the determined content to the user U using the determined expression. This can increase the effectiveness of recommendations for individual users. For example, content that matches the user's hobbies and tastes can be recommended using an expression that makes the user want to use the content (for example, the user wants to visit a restaurant).

[0017] The wording of the recommendation does not necessarily have to be in accordance with psychological bias, but may be anything that influences the recommendation. Also, the wording of the recommendation may be anything other than the wording used when making the recommendation.

[0018] In this embodiment, the recommendation system 10 learns a psychological bias estimation model based on the user U's reaction to the recommendations, which is the user U's behavior. That is, the recommendation system 10 performs reinforcement learning on a recommendation determination method. The recommendation system 10 may also learn a content selection model based on the user U's reaction to the recommendations. These learnings can improve the accuracy of recommendations.

[0019] Next, the functions of the recommendation system 10 according to this embodiment will be described. As shown in FIG. 1, the recommendation system 10 includes a user information acquisition unit 11, a content determination unit 12, an expression determination unit 13, a difficulty level information acquisition unit 14, a behavior information acquisition unit 15, and a learning unit 16.

[0020] The user information acquisition unit 11 is a functional unit that acquires user information related to a user who is the target of a recommendation. The user information acquired by the user information acquisition unit 11 is used in the process related to the recommendation described below. What kind of information the user information is, how it is acquired, and how it is used will be described later. Note that the user information described later is an example, and any information that can be used in the process related to the recommendation may be used.

[0021] The recommendation system 10 may make a recommendation to a user in a push manner. For example, a recommendation may be made when the user enters a specific state (Push firing). For example, a recommendation may be made when the user's location enters a specific area for which recommendations are made. Alternatively, a recommendation may be made when the user uses a specific means of transportation (for example, a train or a taxi). The user information acquisition unit 11 may acquire information for making these decisions from the terminal 20 carried by the user. For example, the user information acquisition unit 11 may acquire information indicating the location of the terminal 20, such as latitude and longitude, and determine whether to make a recommendation to the user based on the information.

[0022] Note that the recommendation system 10 may make recommendations to users at timings other than those described above. For example, the recommendation may be made at a preset time, or may be made in response to some other trigger. Alternatively, the recommendation may be made in response to a request from the terminal 20. The functional units related to recommendations to users, which are described below, may function at the timing when recommendations to users are made.

[0023] The content determination unit 12 is a functional unit that determines content to be recommended to a user based on at least a part of the user information acquired by the user information acquisition unit 11. In this embodiment, the content determination unit 12 determines a restaurant as content to be recommended to a user. The content determination unit 12 determines the recommended content in accordance with the content selection model shown below.

[0024] As user information used by the content determination unit 12, the user information acquisition unit 11 acquires information indicating the POI (Point of Interest) visit record of the user to be recommended. In this embodiment, the POI is the recommended restaurant. The POI visit record is the number of times the user has visited each store (restaurant) in the past.

[0025] FIG. 3(a) shows a user / POI visit record database provided in the recommendation system 10, which stores information indicating a user's POI visit record. The user / POI visit record database previously stores a user ID and the number of past visits of the user identified by the user ID to each store, in association with each other. The user ID is an identifier previously assigned to the user who is the target of recommendations. The information stored in the user / POI visit record database is generated by conventional technology, etc. For example, the information stored in the user / POI visit record database is generated based on information about the user's payment at a store or the user's location information. The information may be generated based on a geofence. The user information acquisition unit 11 reads and acquires information about the user who is the target of recommendations from the user / POI visit record database.

[0026] As the user information used by the content determination unit 12, the user information acquisition unit 11 acquires information indicating the degree of interest of the user in a category that is the target of recommendation. The category is the category of the restaurant that is the target of recommendation. For example, the category is "yakiniku" or "ramen."

[0027] FIG. 3(b) shows a user / category interest information database provided in the recommendation system 10, which stores information indicating a user's interest in categories. The user / category interest information database pre-stores user IDs and numerical values ​​indicating the user's interest in each category, associated with each other. The larger the numerical value indicating the interest level, the more interested the user is in that category. The user / category interest information database is generated using conventional technology, etc. For example, the user / category interest information database is generated based on the number of times the user has previously visited stores in each category and a user questionnaire, etc. The user information acquisition unit 11 reads and acquires information about users who are the target of recommendations from the user / category interest information database.

[0028] The user information acquisition unit 11 acquires information indicating the situation of the user to be recommended at the time of recommendation, i.e., information indicating the user's current situation, as the user information used by the content determination unit 12. For example, the information indicating the user's current situation includes information indicating whether the user had lunch that day and the user's location information.

[0029] FIG. 3(c) shows a user current situation database that stores information indicating a user's current situation and is used by the content determination unit 12 included in the recommendation system 10. The user current situation database stores user IDs and information indicating the user's current situation indicated by the user ID in association with each other. With regard to the lunch information among the information indicating the current situation, if the numerical value is 1, it indicates that the user has had lunch that day, and if the numerical value is 0, it indicates that the user has not had lunch that day. This information is generated in real time using conventional technology or the like.

[0030] The current location information in the information indicating the current situation is information indicating the current location of the user. For example, the current location information is information of latitude and longitude. For example, this information is generated (acquired) in real time by acquiring information indicating the current location of the terminal 20 from the terminal 20 carried by the user. The user information acquisition unit 11 reads and acquires information about the user who is the target of the recommendation from the user current situation database.

[0031] The user information acquisition unit 11 outputs the acquired information to the content determination unit 12. Note that the user information acquisition unit 11 may acquire user information other than the above as the user information used by the content determination unit 12. The user information acquisition unit 11 may also acquire user information by a method other than the above. For example, the user information acquisition unit 11 may acquire user information by receiving it from the terminal 20.

[0032] The content determination unit 12 receives the user information from the user information acquisition unit 11. The content determination unit 12 also acquires information on each store that is a candidate for recommendation to the user.

[0033] FIG. 4(a) shows a store database provided in the recommendation system 10, which stores information related to each restaurant. The store database stores store IDs and information related to the stores indicated by the store IDs in association with each other. The store IDs are identifiers that are preset for stores recommended to users. As shown in FIG. 4(a), the information related to the stores includes location information, congestion information, store names, recommended opening times, and category information.

[0034] The location information is information indicating the location of a store. For example, the location information is information of latitude and longitude. The location information is stored in advance in the store database. The congestion information is information indicating the degree of congestion of a store. The larger the congestion information value, the more crowded the store. The congestion information is generated in real time using conventional technology, etc., and stored in the store database. The store name is the name of the store. The store name is stored in advance in the store database. The recommended time is the time of day when it is recommended to visit the store. The recommended time is stored in advance in the store database. The category is the category of the store. The category is one of the categories in the user category interest information database shown in Figure 3(b). The category is stored in advance in the store database.

[0035] The content determination unit 12 acquires information related to each restaurant from the store database. Candidate restaurants to be recommended to the user may be a portion of the restaurants whose information is stored in the store database. For example, only restaurants based on the user's current location may be set as candidate restaurants to be recommended to the user. Specifically, restaurants within a certain range from the user's current location or restaurants in the same area as the user's current location (for example, restaurants in the same district, town, or village) may be set as candidate restaurants to be recommended to the user. In this case, the content determination unit 12 may determine candidate restaurants to be recommended to the user based on the user's current location indicated by the user information input from the user information acquisition unit 11 and the locations of the restaurants indicated by the acquired information related to the restaurant.

[0036] The content determination unit 12 determines a store to recommend to the user from the acquired information as follows: First, the content determination unit 12 calculates the action difficulty for each candidate store to be recommended. The action difficulty is the difficulty of the user taking action toward the store. The user's action toward the store is, for example, visiting the store, that is, using the content. Furthermore, the user's action toward the store is not limited to the above, and may be any action toward the store, such as opening and viewing the information recommending the store (that is, showing interest in the store (content); this will be described in detail later).

[0037] An example of the calculated difficulty of an action is shown in Figure 4(b). The calculated difficulty of an action is stored in a user / POI action difficulty database provided in the recommendation system 10. The numerical value indicating the difficulty of an action indicates that the smaller the value, the greater the difficulty (i.e., the tendency to not take action at the corresponding store), and the larger the value, the less the difficulty (i.e., the tendency to take action at the corresponding store).

[0038] For example, the content determining unit 12 calculates the activity difficulty for each store using the following formula. Difficulty of action = (f1 (number of visits) + interest level + f2 (distance to the store)) × s (whether or not to eat)

[0039] In the above formula, f1 (number of visits) is a function whose value increases as the input number of visits increases. The number of visits is the number of times the user has visited the store for which the difficulty of an action is being calculated. The interest level is a numerical value indicating the user's interest in the store category for which the difficulty of an action is being calculated. f2 (distance from the store) is a function whose value decreases as the input distance increases. The distance from the store is the distance between the store for which the difficulty of an action is being calculated and the user's current location. s (whether or not the user has eaten) is a value indicating whether or not the user has eaten. For example, when making recommendations for the lunchtime, if the user has eaten lunch, s (whether or not the user has eaten) is set to 0, and if the user has not eaten lunch, s (whether or not the user has eaten) is set to 1.

[0040] According to the above formula, the more frequently a store is visited by the user, the more interested the store is in the category of the user, and the closer the store is to the user's current location, the higher the action difficulty value will be. In other words, the more likely it is that such a store will be a store where the user will take action. Furthermore, if the user has not eaten a meal, the action difficulty value will be higher, and the user will be more likely to take action at the store.

[0041] Note that the calculation of the difficulty of an action does not necessarily have to be performed using the above formula. Furthermore, all of the above elements do not have to be used in calculating the difficulty of an action, and only some of the elements may be used in calculating the difficulty of an action. Furthermore, elements other than those described above may also be used in calculating the difficulty of an action. For example, in the above formula, among the information related to the store, congestion information and recommended time are not used, but this information may also be used. For example, the less crowded the store is, the higher the value of the difficulty of the action. Alternatively, the closer the current time is to the recommended time, the higher the value of the difficulty of the action.

[0042] The content determination unit 12 determines a store to recommend to the user based on the calculated difficulty of the action. For example, the content determination unit 12 determines the store with the highest value of the difficulty of the action as the store to recommend to the user. Note that stores that have already been recommended to the user within a certain period of time may be excluded from the stores to be recommended. However, the content determination unit 12 may determine a store to recommend to the user based on a determination criterion other than the above. Furthermore, the content determination unit 12 may determine not to recommend a store to the user if there is no store with an activity difficulty level equal to or higher than a certain value.

[0043] Furthermore, the content determination unit 12 may determine how to recommend a store, i.e., the type of recommendation. Figure 5 shows a recommendation content database provided in the recommendation system 10, which stores the content of recommendations. The recommendation content database previously stores a recommendation ID, a store ID, a type, and coupon information in association with each other. The recommendation ID is an identifier previously set for the content of the recommendation. The content of the recommendation is identified by the following information. The store ID is the store ID of the recommended store.

[0044] The type is the type of recommendation. Examples of recommendation types include "customer referral" and "peak shift" as shown in FIG. 5. Customer referral is a recommendation to users to visit a store using coupons or other methods. Peak shift is a recommendation to users to visit a store when facilities around the store (for example, a station) are crowded, to avoid the crowds.

[0045] Coupon information is information about coupons presented to users when recommendations are made. Coupon information includes, for example, information indicating whether or not a coupon is available and information indicating the content of the coupon. In the example shown in FIG. 5, the coupon information is in the format of {A:B}. A is information indicating whether or not a coupon is available, with a numerical value of 1 indicating that a coupon is available and a numerical value of 0 indicating that a coupon is not available. B indicates the amount of discount from the price as a coupon (for example, the data in the first row of FIG. 5 is a coupon for a 100 yen discount). Note that if the numerical value of A is 0, there is no information about B.

[0046] After determining the store to be recommended, the content determination unit 12 refers to a recommendation content database to determine the content of the recommendation. The content determination unit 12 stores in advance decision criteria for determining the content of the recommendation, and determines the content of the recommendation based on the decision criteria. For example, the content determination unit 12 acquires information indicating the degree of congestion of facilities (e.g., stations) surrounding the store to be recommended to the user. This information may be acquired using conventional technology, etc. If the degree of congestion is equal to or greater than a preset threshold, the content determination unit 12 determines to recommend a type of peak shift. If the degree of congestion is less than a preset threshold, the content determination unit 12 determines to recommend a type of customer transfer. Note that the content determination unit 12 may determine recommendation content other than the above. Also, recommendations may be made without a type such as customer transfer or peak shift. Note that the content selection model defines the process for determining the above recommendation content by the content determination unit 12.

[0047] The expression determination unit 13 is a functional unit that determines an expression to be used when recommending the content determined by the content determination unit 12 to the user based on at least a part of the user information acquired by the user information acquisition unit 11. The expression determination unit 13 may determine an expression to be used when making the recommendation that corresponds to the psychological bias of the user who is the target of the recommendation. The expression determination unit 13 determines the expression to be used for the recommendation in accordance with the psychological bias estimation model shown below.

[0048] As user information used by the expression determination unit 13, the user information acquisition unit 11 acquires information indicating the attributes of users who are to receive recommendations. FIG. 6(a) shows a user attribute database provided in the recommendation system 10, which stores information indicating user attributes. The user attribute database stores in advance user IDs and information indicating the attributes of users indicated by the user IDs in association with each other. As shown in FIG. 6(a), user attributes include, for example, gender, residential area, occupation, family structure, and hobbies and interests. As shown in FIG. 6(a), the information indicating each attribute may be an ID indicating the attribute. The user information acquisition unit 11 reads and acquires information about users who are to receive recommendations from the user attribute database.

[0049] As the user information used by the expression determination unit 13, the user information acquisition unit 11 acquires information indicating the situation of the user to be recommended at the time of the recommendation, i.e., information indicating the user's current situation. For example, the information indicating the user's current situation is information indicating whether the user is at home, the number of times the user has received a recommendation in the past, and today's visit history. The number of times the user has received a recommendation in the past is the number of times the user has received a recommendation in the past. The today's visit history is information indicating whether the user has visited a store that is a candidate for recommendation to the user.

[0050] FIG. 6(b) shows a user current situation database used by the expression determination unit 13 of the recommendation system 10, which stores information indicating the user's current situation. The user current situation database stores user IDs and information indicating the user's current situation indicated by the user IDs, in association with each other. Among the information indicating the current situation, information indicating whether the user is at home indicates that the user is at home at that time if the value is 1, and indicates that the user is not at home at that time if the value is 0. This information is generated in real time using conventional technology, etc. The number of times messages have been received in the past and the history of visits today are appropriate pieces of information indicating these, and are generated in real time using conventional technology, etc. The user information acquisition unit 11 reads and acquires information about users who are the target of recommendations from the user current situation database.

[0051] The user information acquisition unit 11 outputs the acquired information to the expression determination unit 13. Note that the user information acquisition unit 11 may acquire user information other than the above as the user information used by the expression determination unit 13. The user information acquisition unit 11 may also acquire user information by a method other than the above. For example, the user information acquisition unit 11 may acquire user information by receiving it from the terminal 20.

[0052] The expression determination unit 13 inputs user information from the user information acquisition unit 11. The expression determination unit 13 determines the expression to use when making a recommendation from the input information as follows: The expression determination unit 13 converts the input user information into features. The features are vectors with a preset number of dimensions. An example of the converted features is shown in FIG. 6(c). Feature 1, feature 2, feature 3, etc. are elements of the feature vector. The conversion from user information to features can be performed using conventional technology, etc.

[0053] The feature may include information on external factors other than the user information that may affect the recommendation. For example, information indicating the weather and time at that time may be reflected in the feature. Furthermore, information related to the content of the recommendation determined by the content determination unit 12 may be reflected in the feature. That is, the expression determination unit 13 may determine the expression to be used when making a recommendation based on the content determined by the content determination unit 12. For example, the feature may include the store category (e.g., the category "restaurant") and the type of recommendation (e.g., the type "customer referral") from the determined content of the recommendation. The information other than the above user information may be treated as a feature in a different dimension from the user information, or may be converted into a feature together with the user information.

[0054] The expression determination unit 13 estimates the user's psychological bias from the obtained feature amount and the psychological bias estimation model. Specifically, the expression determination unit 13 calculates an evaluation value for each psychological bias (for example, for each type of psychological bias) (for example, "loss aversion" or "conformity"). To calculate the evaluation value, a psychological bias estimation model is used. The psychological bias estimation model includes parameters for each psychological bias. The parameters are vectors with the same number of dimensions as the feature amount. Each element of the parameter of the psychological bias estimation model corresponds to each element of the feature amount. The psychological bias estimation model is common to all users. However, the psychological bias estimation model may be different for each user or each type of user.

[0055] The expression determination unit 13 calculates an evaluation value by multiplying corresponding elements of the feature quantity by elements of the parameters of the psychological bias estimation model and taking the sum. That is, the expression determination unit 13 calculates the inner product of the vector of the feature quantity and the vector of the parameters of the psychological bias estimation model as the evaluation value. The expression determination unit 13 calculates an evaluation value for each type of psychological bias using the parameters for each type of psychological bias. Figure 7 shows an example of parameters for one psychological bias (psychological bias 1) and an example of how the evaluation value is calculated. Figure 8 shows the evaluation value of the psychological bias calculated for each user. A larger calculated evaluation value indicates that the user has a stronger psychological bias.

[0056] The parameters of the psychological bias estimation model are updated through learning by the learning unit 16, which will be described later. Learning by the learning unit 16 is performed based on the user's reaction to the recommendation. Therefore, each time a recommendation is made, the psychological bias is determined more appropriately, and as a result, the recommendation is made more appropriately.

[0057] The expression determination unit 13 determines nudge words, which are expressions of the recommendation, based on the estimated psychological bias. In addition to the estimated psychological bias, the expression determination unit 13 may refer to information related to the content of the recommendation determined by the content determination unit 12 and determine the expression of the recommendation based on the information.

[0058] The recommendation expression is determined based on a preset association between psychological biases and nudge words. Figure 9 shows a nudge wording database provided in the recommendation system 10, which stores information on the association. The nudge wording database stores in advance a nudge ID, psychological bias, type, coupon availability, and nudge wording, all associated with each other. The nudge ID is an identifier preset for the nudge wording. The type is the type of recommendation (such as the above-mentioned "customer referral" or "peak shift"). Coupon availability indicates whether or not a coupon is presented to the user when a recommendation is made. A coupon availability value of 1 indicates the presence of a coupon, and a coupon availability value of 0 indicates the absence of a coupon. The portion in {} in the nudge wording is filled with information indicating the recommended store (e.g., the store name) determined by the content determination unit 12.

[0059] The expression determination unit 13 determines (selects) the psychological bias to be used for the recommendation probabilistically based on the ratio of the calculated evaluation values. By determining the psychological bias to be used for the recommendation probabilistically in this way, it is possible to prevent the same nudge wording from being used all the time. However, the psychological bias to be used for the recommendation may be determined by a method other than the above.

[0060] The expression determination unit 13 refers to the nudge wording database shown in Figure 9 and determines (selects) the nudge wording that is associated in the nudge wording database with the combination of the determined psychological bias and the type of recommendation and whether or not a coupon is included determined by the content determination unit 12 as the expression to use when making a recommendation to the user.

[0061] Although not included in the nudge phrase database shown in Figure 9, the nudge phrase may include phrases depending on whether or not the recommendation includes a coupon. For example, if the recommendation includes a coupon, the nudge phrase may include a phrase such as "We have a great coupon for you" in addition to the nudge phrase shown in Figure 9. Also, if the recommendation does not include a coupon, the nudge phrase may include a phrase such as "Recommended for you" in addition to the nudge phrase shown in Figure 9.

[0062] The expression determination unit 13 generates information to be recommended to the user using the content of the recommendation determined by the content determination unit 12 and the determined expression, and transmits the information to the terminal 20. Note that the recommendation itself, such as transmitting information to the terminal 20 based on the determination of the content determination unit 12 and the expression determination unit 13, does not need to be performed by the recommendation system 10, and may be performed by a system or device other than the recommendation system 10.

[0063] The recommendation information transmitted to the terminal 20 is referenced by the user of the terminal 20. For example, when the recommendation information is received by the terminal 20, a recommendation application notifies the user. The notification to the user is, for example, a display related to the recommendation on the screen of the terminal 20. The display at the time of notification is performed so that the user can recognize the content determined by the content determination unit 12 and the expression determination unit 13. Specifically, a nudge message determined by the expression determination unit 13, including information indicating the recommended store determined by the content determination unit 12, is displayed.

[0064] The user operates the application on the terminal 20 to refer to the details of the recommendation (for example, store information, etc.). In this embodiment, this operation is called "opening." As described above, opening is also one of the user's actions regarding the recommended store.

[0065] The difficulty level information acquisition unit 14 is a functional unit that acquires difficulty level information indicating the difficulty level at which a user acts on the content determined by the content determination unit 12. The difficulty level information acquisition unit 14 may acquire difficulty level information indicating the difficulty level that reflects at least one of the user's past usage status of the content, the user's interests, the user's status at the time of recommendation, and the status of the content at the time of recommendation.

[0066] A user's reaction to a recommendation depends on the recommended content (in this embodiment, a restaurant) itself and the expression of the recommendation, such as the nudge wording. Therefore, when a user takes action in response to a recommendation, such as opening a recommendation or visiting a recommended restaurant, it is possible that the nudge wording does not necessarily have a significant influence, but rather that the recommended content itself has a significant influence. For example, it is possible that a user takes action toward a recommended restaurant because they originally liked the recommended restaurant.

[0067] In other words, from the perspective of training a psychological bias estimation model, user responses to recommendations contain a large amount of noise. Training using noisy data does not necessarily result in appropriate training, and there is a risk that training of the psychological bias estimation model will not progress.

[0068] The difficulty level is used to eliminate the influence (bias) of recommended content, i.e., the influence of the user's tastes and preferences, when training a psychological bias estimation model based on the user's response to recommendations. Training using the difficulty level can speed up the convergence of the training of the psychological bias estimation model.

[0069] The difficulty information acquisition unit 14 acquires, as difficulty information, information indicating the difficulty of an action calculated by the content determination unit 12 for a recommended store. As described above, the difficulty of an action calculated by the content determination unit 12 reflects at least one of the user's past content usage status, the user's interests, the user's status at the time of recommendation, and the status of the content at the time of recommendation. However, the difficulty information acquired by the difficulty information acquisition unit 14 does not need to be information indicating the difficulty of an action calculated by the content determination unit 12, and the difficulty information acquisition unit 14 may calculate the difficulty using a calculation method different from the calculation method used by the content determination unit 12 and acquire the difficulty information. The difficulty information acquisition unit 14 outputs the acquired difficulty information to the learning unit 16.

[0070] The behavioral information acquisition unit 15 is a functional unit that acquires behavioral information indicating user behavior in response to a recommendation made to the user in accordance with the determination by the content determination unit 12 and the expression determination unit 13. User behavior in response to a recommendation includes, for example, opening and viewing the recommendation information, visiting a store, and using a coupon included in the recommendation information at the store. In addition, user behavior in response to a recommendation may be any behavior other than those described above, as long as it is caused by the recommendation. Furthermore, visiting a store and using a coupon may be considered user behavior in response to a recommendation only when the recommendation information is opened.

[0071] Specifically, the behavioral information acquisition unit 15 acquires, as behavioral information, information indicating the time when a recommendation was made to the user, whether the recommended information was opened, the time of opening, and whether a coupon was used at the recommended store. The information indicating the time when a recommendation was made to the user, whether the recommended information was opened, and the time of opening can be acquired, for example, via a recommendation application on the terminal 20. The information indicating whether a coupon was used can be acquired by acquiring information related to the user's payment at the recommended store. The behavioral information acquisition unit 15 outputs the acquired behavioral information to the learning unit 16.

[0072] The learning unit 16 is a functional unit that learns the determination method of the expression determination unit 13 in consideration of the difficulty level of the behavior of the user who is the target of the recommendation, based on the difficulty level information acquired by the difficulty level information acquisition unit 14 and the behavior information acquired by the behavior information acquisition unit 15. The learning unit 16 may weight the evaluation value according to the behavior information based on the difficulty level information, and learn the determination method of the expression determination unit 13 using the weighted evaluation value.

[0073] The learning unit 16 learns the psychological bias estimation model based on the difficulty level information acquired by the difficulty level information acquisition unit 14 and the behavioral information acquired by the behavioral information acquisition unit 15. The learning of the psychological bias estimation model by the learning unit 16 is performed so that if the user takes action in response to a recommendation, the psychological bias is strongly estimated. At that time, learning is performed so that the influence of the recommended content is eliminated based on the difficulty level information as described above. For example, the learning unit 16 learns the psychological bias estimation model as follows.

[0074] The learning unit 16 receives difficulty level information from the difficulty level information acquisition unit 14. The learning unit 16 receives behavioral information from the behavioral information acquisition unit 15. The learning unit 16 also receives information related to recommendations corresponding to the difficulty level information and behavioral information from the content determination unit 12 and the expression determination unit 13. FIG. 10 shows the information acquired by the learning unit 16. Of the information shown in FIG. 10, the information used to train the psychological bias estimation model is the associated information on behavioral difficulty, psychological bias, push time, opening, opening time, and coupon use. The evaluation information shown in FIG. 10 will be described later.

[0075] The difficulty of behavior is the difficulty of behavior for the recommended store, indicated by the difficulty information acquired by the difficulty information acquisition unit 14. The psychological bias is the psychological bias used in the recommendation determined by the expression determination unit 13. The learning unit 16 updates the parameters of the psychological bias estimation model for this psychological bias. The push time, opening, opening time, and coupon use are behavioral information acquired by the behavioral information acquisition unit 15. The push time is the time when the recommendation was made to the user. The opening is information indicating whether the recommendation information has been opened by the user. A numerical value of 1 indicates that the recommendation information has been opened, and a numerical value of 0 indicates that the recommendation information has not been opened. The opening time is the time when the recommendation information was opened by the user.

[0076] Coupon use is information indicating whether or not a coupon has been used by a user. A numerical value of 1 indicates that a coupon has been used, and a numerical value of 0 indicates that a coupon has not been used. The behavioral information acquisition unit 15 may acquire information related to the opening of recommendation information and information related to coupon use in real time, or may acquire information after a preset time (for example, several minutes to several hours) has elapsed since the recommendation to the user. When information is acquired after a preset time has elapsed, the information is the information at that time.

[0077] The learning unit 16 calculates a behavioral change evaluation value, which is an evaluation value according to the behavioral information, from the behavioral information. For each user behavior, a behavioral change evaluation value is set in advance when that behavior is performed. For example, opening a package is a value of 0.2, and using a coupon is a value of 0.8. The behavioral change evaluation value is an index value that indicates to what extent the user has performed an action in response to a recommendation, and the larger the value, the more likely the user is to behave in the manner expected by the recommender. The learning unit 16 refers to the behavioral information and calculates a behavioral change evaluation value for the behavior performed by the user. When the user has performed multiple behaviors, the behavioral change evaluation value is the sum of the values ​​corresponding to each behavior. In the example shown in Figure 10, the user has opened a package and used a coupon, so 0.2 (opening) + 0.8 (using a coupon) = 1.0.

[0078] The learning unit 16 weights the calculated behavioral change evaluation value based on the difficulty level information. For example, the learning unit 16 weights the behavioral change evaluation value by a value of (1 - difficulty level of action). In the example shown in FIG. 10, the weighted behavioral change evaluation value is (0.2 (opening) + 0.8 (using coupon)) × (1 - 0.8 (difficulty level of action)) = 0.2. The evaluation shown in FIG. 10 is the weighted behavioral change evaluation value. The weighting value based on the difficulty level information is a value obtained by subtracting the value of the difficulty level of action, so the larger the value, the less likely the user is to take action.

[0079] Therefore, the weighted behavioral change evaluation value is a value that indicates the user's reaction to the recommendation, excluding the influence (bias) of the recommended content, i.e., the influence of the user's tastes and preferences. In addition, if the difficulty information includes the situation of the content (store), the influence of that information is also excluded.

[0080] As shown in FIG. 11 , the learning unit 16 then calculates the gradient between the user's feature quantities used to calculate the psychological bias evaluation value and the parameters of the psychological bias estimation model to be updated. When calculating the gradient, normalization of the user's feature quantities is usually required to ensure that the gradient can be calculated appropriately. As an example of such normalization, the value of each element of the user's feature quantities may be multiplied by a preset value (n shown in FIG. 11 ). In addition to the above, normalization may also be performed using a preset function f that inputs the user's feature quantities and outputs normalized feature quantities. The function f performs general normalization. The gradient is calculated by finding the cross-entropy error from two vectors. Alternatively, the gradient may be calculated using a method other than the cross-entropy error. The calculated gradient is a vector with the same number of dimensions as the feature quantities and the parameters of the psychological bias estimation model.

[0081] The learning unit 16 multiplies each element of the gradient by the weighted behavioral change evaluation value to obtain updated parameters. The learning unit 16 adds the parameters of the psychological bias estimation model to be updated and the updated parameters for each element to obtain updated parameters (learned parameters).

[0082] The psychological bias estimation model learned by the learning unit 16 is used for subsequent recommendations. Furthermore, learning by the learning unit 16 is repeated every time a recommendation is made. Repeated learning improves the accuracy of the psychological bias estimation model, enabling more appropriate recommendation expressions to be determined.

[0083] Note that the learning by the learning unit 16 does not necessarily have to be performed as described above, but may be performed based on the difficulty level information and behavior information, taking into consideration the difficulty level at which the user who is the target of the recommendation behaves.

[0084] The learning unit 16 may perform learning when a predetermined time has elapsed since the time the recommendation was made to the user, or may perform learning at a predetermined time (e.g., a specific time of day). Alternatively, the learning unit 16 may perform learning when the user takes action on the recommendation within a time limit (e.g., a predetermined time from the time the recommendation was made to the user). For example, the behavioral information acquisition unit 15 acquires behavioral information indicating that the user has taken action on the recommendation in real time and inputs the information to the learning unit 16, and the learning unit 16 performs learning when the behavioral information is input from the behavioral information acquisition unit 15. This is because learning is possible when behavioral information indicating that the user has taken action on the recommendation is acquired. Furthermore, the learning unit 16 may also train a content selection model (a method for the content determination unit 12 to determine content to be recommended) in addition to a psychological bias estimation model (a method for determining the expression of the recommendation by the expression determination unit 13). The content selection model may be trained using conventional technology or the like. The functions of the recommendation system 10 according to this embodiment have been described above.

[0085] Next, the processing executed by the recommendation system 10 according to this embodiment (the operating method performed by the recommendation system 10) will be described using the flowchart of FIG. 12. This processing is performed when a recommendation is made to a user (for example, triggered by the above-mentioned Push firing). First, the user information acquisition unit 11 acquires user information related to the user who is the target of the recommendation (S01). Next, the content determination unit 12 calculates the activity difficulty for each candidate store to be recommended (S02). This calculation is performed based on the user information and store information. Next, the content determination unit 12 determines the store to be recommended to the user, i.e., the recommendation content, based on the activity difficulty (S03). At this time, the type of recommendation, such as "customer transfer" or "peak shifting," may be determined as described above.

[0086] Next, the expression determination unit 13 uses a psychological bias estimation model to estimate the psychological bias of the user (S04). This estimation is performed based on the user information. Next, the expression determination unit 13 determines a nudge statement, which is an expression of a recommendation, based on the estimated psychological bias (S05). Note that the determination of the store to be recommended (S03) and the determination of the nudge statement (S05) may be performed in parallel. Next, recommendation information is generated from the determined store and nudge statement, and the recommendation is made to the user (S06). The recommendation to the user is made, for example, by transmitting the recommendation information to the terminal 20.

[0087] The following processing is processing related to learning the psychological bias estimation model. This processing is based on the user's behavior in response to the recommendation, and is therefore usually performed a predetermined time after the recommendation. In this processing, the difficulty information acquisition unit 14 acquires difficulty information indicating the difficulty with which the user will act in response to the content determined by the content determination unit 12 (S07). Furthermore, the behavior information acquisition unit 15 acquires behavior information indicating the user's behavior in response to the recommendation to the user (S08). Next, the learning unit 16 learns a psychological bias estimation model that takes into account the difficulty with which the user who is the target of the recommendation will act, based on the difficulty information and the behavior information (S09). The learned psychological bias estimation model is used for subsequent recommendations to the user. The above is the processing executed by the recommendation system 10 according to this embodiment.

[0088] In this embodiment, when training a psychological bias estimation model that indicates a method for determining the representation of a recommendation, the degree of difficulty with which a user will act on recommended content is taken into consideration. Therefore, for example, the psychological bias estimation model can be trained by excluding the influence of the user's hobbies and preferences from the user's behavior regarding the recommendation. As a result, according to this embodiment, it is possible to more appropriately train a method for determining the representation of a recommendation.

[0089] Furthermore, as in the present embodiment, the psychological bias estimation model may be trained by weighting an evaluation value according to behavioral information (for example, the above-described behavioral change evaluation value) based on difficulty level information and using the weighted evaluation value. This configuration allows for more appropriate and reliable training of a method for determining the representation of recommendations. For example, the psychological bias estimation model may be trained by reliably excluding the influence of a user's hobbies and preferences from the user's behavior regarding recommendations. However, the psychological bias estimation model does not necessarily have to be trained as described above, and may be trained based on difficulty level information and behavioral information, taking into account the difficulty level of the behavior of the user who is the target of the recommendation. Furthermore, the psychological bias estimation model may be trained using the bandit algorithm described above, or by other methods.

[0090] Furthermore, as in this embodiment, the wording used when making a recommendation may be wording that corresponds to the psychological bias of the user who is the target of the recommendation. With this configuration, the reminder can be given using appropriate wording that corresponds to the user's psychological bias, thereby enhancing the effectiveness of the recommendation. However, the wording used when making a recommendation does not necessarily need to correspond to the psychological bias.

[0091] Furthermore, as in this embodiment, the difficulty level indicated by the difficulty level information may reflect at least one of the user's past usage of the content, the user's interests, the user's situation at the time of the recommendation, and the situation of the content at the time of the recommendation. With this configuration, the difficulty level information can be made appropriate and reliable. As a result, learning of a method for determining the expression of recommendations can be more appropriate and reliable. However, the difficulty level indicated by the difficulty level information does not necessarily have to be the above, and may be the difficulty level at which the user acts on the content.

[0092] In this embodiment, the method for determining the representation of a recommendation is performed using a psychological bias estimation model, but it is not necessary to use a psychological bias estimation model. Any method for determining the representation of a recommendation can be used as long as it can be trained within the above-mentioned framework. Furthermore, even when a psychological bias estimation model is used, it does not necessarily have to be the one described above, and any psychological bias estimation model can be used as long as it can be trained within the above-mentioned framework.

[0093] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0094] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, regard, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0095] For example, the recommendation system 10 according to an embodiment of the present disclosure may function as a computer that performs the information processing of the present disclosure. FIG. 13 is a diagram illustrating an example of the hardware configuration of the recommendation system 10 according to an embodiment of the present disclosure. The recommendation system 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like. The hardware configuration of the terminal 20 may also be as described here.

[0096] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the recommendation system 10 may be configured to include one or more of the apparatuses shown in the figure, or may be configured to exclude some of the apparatuses.

[0097] Each function in the recommendation system 10 is realized by loading specified software (programs) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via a communication device 1004, and control at least one of reading and writing data in the memory 1002 and the storage 1003.

[0098] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, each function in the recommendation system 10 described above may be realized by the processor 1001.

[0099] Furthermore, the processor 1001 reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes in accordance with these. The program used is a program that causes a computer to execute at least some of the operations described in the above-mentioned embodiments. For example, each function in the recommendation system 10 may be realized by a control program stored in the memory 1002 and running on the processor 1001. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0100] The memory 1002 is a computer-readable recording medium and may be configured, for example, by at least one of a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for performing information processing according to an embodiment of the present disclosure.

[0101] The storage 1003 is a computer-readable recording medium and may be composed of at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. The storage 1003 may also be referred to as an auxiliary storage device. The storage medium provided in the recommendation system 10 may be, for example, a database, a server, or other appropriate medium including at least one of the memory 1002 and the storage 1003.

[0102] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0103] The input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (for example, a touch panel).

[0104] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0105] The recommendation system 10 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0106] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0107] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0108] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0109] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, notification of predetermined information (e.g., notification that "X is true") is not limited to being done explicitly, but may be done implicitly (e.g., by not notifying the predetermined information).

[0110] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0111] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0112] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0113] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0114] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0115] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0116] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0117] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0118] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0119] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0120] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0121] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0122] 10...Recommendation system, 11...User information acquisition unit, 12...Content determination unit, 13...Expression determination unit, 14...Difficulty level information acquisition unit, 15...Behavioral information acquisition unit, 16...Learning unit, 20...Terminal, 1001...Processor, 1002...Memory, 1003...Storage, 1004...Communication device, 1005...Input device, 1006...Output device, 1007...Bus.

Claims

1. a user information acquisition unit that acquires user information related to a user who is a target of recommendation; a content determination unit that determines content to be recommended to a user based on at least a part of the user information acquired by the user information acquisition unit; an expression determination unit that determines an expression to be used when recommending the content determined by the content determination unit to the user based on at least a part of the user information acquired by the user information acquisition unit; a difficulty level information acquisition unit that acquires difficulty level information indicating the difficulty level at which a user should act with respect to the content determined by the content determination unit; a behavior information acquiring unit that acquires behavior information indicating user behavior in response to a recommendation made to the user in accordance with the determination by the content determining unit and the expression determining unit; a learning unit that learns a determination method by the expression determination unit, taking into consideration the difficulty level of a user who is a target of recommendation, based on the difficulty level information acquired by the difficulty level information acquisition unit and the behavior information acquired by the behavior information acquisition unit; and A recommendation system comprising:

2. The recommendation system according to claim 1 , wherein the learning unit weights the evaluation value corresponding to the behavioral information based on the difficulty information, and uses the weighted evaluation value to learn the determination method of the expression determination unit.

3. The recommendation system according to claim 1 , wherein the expression determination unit determines an expression to be used when making a recommendation in accordance with a psychological bias of a user who is a target of the recommendation.

4. The recommendation system of claim 1, wherein the difficulty information acquisition unit acquires difficulty information indicating the difficulty level that reflects at least one of the user's past usage of the content, the user's interests, the user's situation at the time of recommendation, and the situation of the content at the time of recommendation.

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