JP7489255B2 - Information Providing Device - Google Patents

Description

This disclosure relates to an information providing device.

Conventionally, there is a technology that provides recommendation information according to a user's preferences. For example, Patent Literature 1 describes a device that provides a mobile terminal with store information according to the preferences of the mobile terminal owner (user).

JP 2017-191500 A

The device described in Patent Document 1 provides store information to users by displaying information such as store names in emails or on web pages. This requires the user to open emails or access web pages. Furthermore, even among stores that sell the same type of merchandise, some stores are familiar to users while others are popular stores with long waiting times. Some users are swayed by the majority opinion, while others try to avoid losses. Although the device described in Patent Document 1 provides store information that matches the user's preferences, there is a possibility that sufficient advertising effectiveness will not be achieved.

This disclosure describes an information providing device that can improve advertising effectiveness.

An information providing device according to one aspect of the present disclosure includes a generation unit that generates recommendation information including an image related to a recommendation target according to a user's psychological tendency, and an output unit that outputs the recommendation information to the terminal device together with a command for setting the image on the user's terminal device as a background image of the terminal device.

In this information providing device, recommendation information including an image related to a recommended target according to the user's psychological tendency is generated, and the recommendation information is output to the terminal device together with a command to set the image as the background image of the terminal device. In the information providing device, the image related to the recommended target is set as the background image of the terminal device, so that the user can recognize the image related to the recommended target without any operation by the user. Furthermore, since the recommended target is determined according to the user's psychological tendency, it is possible to increase the likelihood that the user will act in accordance with the recommendation information. As a result, it is possible to improve the advertising effect.

This disclosure can improve advertising effectiveness.

FIG. 1 is a schematic diagram of an information providing system including an information providing device according to an embodiment. FIG. 2 is a diagram showing an example of the questionnaire. FIG. 3 is a block diagram showing a functional configuration of the information providing device shown in FIG. FIG. 4 is a diagram illustrating an example of group information. FIG. 5 is a diagram illustrating an example of user information. FIG. 6 is a diagram illustrating an example of store information. FIG. 7 is a diagram showing an example of visitor information. FIG. 8 is a flowchart showing in detail the setting process performed by the information providing device shown in FIG. FIG. 9 is a flowchart showing a series of processes of an information providing method performed by the information providing apparatus shown in FIG. FIG. 10 is a flowchart showing in detail the determination process shown in FIG. Fig. 11A is a diagram showing an example of a lock screen on a mobile terminal, Fig. 11B is a diagram showing another example of a lock screen on a mobile terminal, and Fig. 11C is a diagram showing yet another example of a lock screen on a mobile terminal. FIG. 12 is a diagram showing a hardware configuration of the information providing device shown in FIG.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that in the description of the drawings, the same elements are given the same reference numerals and duplicate descriptions will be omitted.

With reference to FIG. 1, the configuration of an information providing system including an information providing device according to one embodiment will be described. FIG. 1 is a schematic configuration diagram of an information providing system including an information providing device according to one embodiment. The information providing system 1 shown in FIG. 1 is a system that provides a user with recommendation information related to a recommendation target based on the user's psychological tendency. A recommendation target is an object that is recommended to a user. An example of a recommendation target is, for example, a commercial facility such as a store. In this embodiment, a description will be given using a store as the recommendation target.

Psychological tendencies are also called psychological traits or biases. Psychological tendencies are tendencies of thinking that affect human behavior and judgment. Examples of psychological tendencies include conformity bias, loss aversion bias, status quo bias, retention bias, time preference (present orientation bias), conformity effect, risk preference, Concorde effect, anchoring effect, and naivety.

Conformity bias is a psychological tendency to be swayed by the majority opinion. Loss aversion bias is a psychological tendency to avoid losses. Status quo bias is a psychological tendency to create psychological barriers against the unknown and stick to the current situation. Endowment bias is a psychological tendency to see great value in what one currently owns and feel reluctant to let go of it. Endowment bias can also mean a psychological tendency to think that possessing something rare is valuable. Time preference is a psychological tendency to prefer things that are closer to the present. Risk preference is a psychological tendency to be more likely to take risks. The conformity effect is a psychological tendency to want to be superior to others. The Concorde effect is a psychological tendency to be unable to make rational decisions because one does not want to waste the time and money that has been spent so far. The anchoring effect is a psychological tendency to distort judgments depending on the order in which information is obtained. Naivety is a psychological tendency in which plans and actual actions do not match.

The information provision system 1 includes a plurality of mobile terminals 10 (terminal devices), a plurality of store terminals 20, and an information provision device 30. The plurality of mobile terminals 10, the plurality of store terminals 20, and the information provision device 30 are configured to be able to communicate with each other via a network NW. The network NW may be configured as either a wired or wireless network. Examples of the network NW include a mobile communication network, the Internet, and a WAN (Wide Area Network).

The mobile terminal 10 is a device that is used by a user and can be carried by the user. Examples of the mobile terminal 10 include a smartphone and a tablet terminal. An application for receiving recommendation information (hereinafter referred to as a "recommendation application") is installed on the mobile terminal 10. When the user uses (launches) the recommendation application for the first time, the user registers basic user information. Examples of the user basic information include gender and age. The recommendation application conducts a questionnaire for the user to estimate the user's psychological tendency (psychological characteristics). The questionnaire is conducted, for example, following the registration of the user basic information. The questionnaire includes questions for estimating the psychological tendency and answer options for the questions, each of which is prepared for each psychological tendency.

As shown in FIG. 2, for example, "You can receive 10,000 yen now. If you wait one week, you can receive a little more. How much would you need to receive in one week to be able to wait one week?" is prepared as a question Q1 for estimating time preference. For example, "1) 10,100 yen, 2) 10,500 yen, 3) 11,000 yen, 4) 12,000 yen, 5) 12,001 yen or more" are prepared as options for answer A1 to question Q1. For example, "When you go on a trip, how many tourist spots do you visit per day?" is prepared as a question Q2 for estimating loss aversion bias. For example, "1) 1 place, 2) 2 places, 3) 3 places, 4) 4 places, 5) 5 places or more" are prepared as options for answer A2 to question Q2. For example, question Q3 for estimating conformity bias may be prepared as follows: "We will conduct a true/false answering test for 10 people, including yourself, and ask those who apply to you to raise their hands. You think it's false. If several other people raise their hands for true, will you be influenced and raise your hand for true?". For example, answer options A3 for question Q3 may be prepared as "1) Raise your hand for false, 2) 9 people, 3) 8 people, 4) 7 people, 5) 6 people."

Similar questions and answers are prepared for other biases. For example, a question for estimating the ownership bias is "How many branded items do you own?". Response options for this question are "1) 0, 2) 1-2, 3) 3-4, 4) 5-6, 5) 7 or more." For example, a question for estimating the conformity effect is "When working on content in which you compete with others, do you ever become motivated by getting a high ranking? Please select the answer that applies." Response options for this question are "1) I enjoy it even if I'm ranked low, 2) It's not important to get a high ranking, but I would like to get there if I can, 3) I basically want to get a high ranking, 4) There's no doubt that getting a high ranking is a big motivation, and 5) Being motivated by getting a high ranking is what helps me concentrate the most."

The mobile terminal 10 transmits the user's basic information and information indicating the questionnaire responses to the information providing device 30. The user allows the recommendation application to switch the background image of the standby screen (lock screen). The background image is sometimes called wallpaper.

Furthermore, the user may set preference information in the recommendation application. Preference information is information that indicates the user's tastes. For example, things that the user likes (for example, favorite foods) may be set as preference information. Things that the user dislikes (for example, disliked foods) may also be set as preference information. The user may set preference information by selecting things that the user likes and dislikes from a list prepared in advance in the recommendation application. The user may set preference information at any time. For example, the user may set preference information when using (launching) the recommendation application for the first time. In response to the preference information being set, the mobile terminal 10 transmits the preference information to the information providing device 30.

The mobile terminal 10 acquires location information (latitude and longitude) of the mobile terminal 10 using a GPS (Global Positioning System) or the like. The mobile terminal 10 may acquire information on the installation location of a master station of a connected wireless network as location information. Examples of the installation location of a master station include a mobile network base station and a Wi-Fi access point. The mobile terminal 10 may acquire location information of another terminal present in the vicinity of the mobile terminal 10 as location information of the mobile terminal 10. Examples of such terminals include a Bluetooth (registered trademark) beacon terminal. The mobile terminal 10 periodically transmits location information to the information providing device 30.

The store terminal 20 is a terminal device provided in each store and manages store information related to the store. Examples of the store terminal 20 include a desktop PC (Personal Computer), a notebook PC, a smartphone, and a tablet terminal. Details of the store information will be described later. A store clerk (manager) sets each piece of information included in the store information via the store terminal 20 at any time. Note that the description of the image may be set by selecting from descriptions prepared in advance. The store terminal 20 transmits the store information to the information providing device 30 every time the store information is updated.

The information providing device 30 is a device that provides recommendation information to a user who owns a mobile terminal 10. An example of the information providing device 30 is an information processing device such as a server device.

The functional configuration of the information providing device 30 will be described with reference to FIG. 3. FIG. 3 is a block diagram showing the functional configuration of the information providing device shown in FIG. 1. As shown in FIG. 3, the information providing device 30 functionally includes an acquisition unit 31, an analysis unit 32, a setting unit 33, an extraction unit 34, a determination unit 35, a generation unit 36, an output unit 37, a determination unit 38, an update unit 39, a group information storage unit 41, a user information storage unit 42, a store information storage unit 43, and a visitor information storage unit 44.

The group information storage unit 41 is a functional unit that stores (stores) group information for each group. As shown in FIG. 4, the group information includes a group ID (identifier), attribute information, and group bias information. The group ID is information that can uniquely identify a group. The attribute information is information that indicates attributes that define a group. In the example shown in FIG. 4, gender and age are used as attributes. The group bias information is information that indicates the psychological tendency of a group, and includes a bias value for each psychological tendency. The bias value for a psychological tendency is a value that indicates the degree of the psychological tendency. In this embodiment, a larger bias value indicates a stronger psychological tendency. The minimum bias value is 0, and the maximum bias value is 1. Each bias value included in the group bias information is, for example, the average value of the bias values for the psychological tendencies of users belonging to the group.

The user information storage unit 42 is a functional unit that stores (stores) user information for each user. As shown in FIG. 5, the user information includes a terminal ID, user basic information, bias information, and a behavior change rate. The terminal ID is information that can uniquely identify the mobile terminal 10. The bias information is information that indicates the psychological tendencies of the user, and includes a bias value for each psychological tendency.

The behavior change rate is set for each recommended store and indicates the percentage of users who acted in accordance with the recommended information when they received the recommended information. The behavior change rate for a store is, for example, the percentage of the number of times a user went to the store in accordance with the recommended information to the number of times the user received recommended information about the store. In other words, the behavior change rate for a store is calculated by dividing the number of times a user acted in accordance with recommended information about the store by the number of times the user received recommended information about the store. The terminal ID, user basic information, bias information, and behavior change rate are associated with each other, so the user basic information, bias information, and behavior change rate are information about the user who owns the mobile terminal 10 identified by the terminal ID.

The store information storage unit 43 is a functional unit that stores (stores) store information for each store. As shown in FIG. 6, the store information includes a store ID, a store name, a congestion level, access point information, an image, a description of the image, and a recommendation level. The store ID is information that can uniquely identify a store. The store name is the name of the store. The congestion level is an index that indicates how crowded a store is. In this embodiment, the larger the congestion level value, the more crowded the store is. For example, when the number of people present in a store is the same as the number of people that the store can accommodate (e.g., the number of seats), the congestion level is 1.0. The access point information is information that can identify the access point located closest to the store. For convenience of explanation, the locations where the access points are installed are shown in FIG. 6.

The images are images related to the store. Examples of images include product images, which are images of products offered by the store. The images may be used as background images for the mobile terminal 10. Therefore, an image suitable as a background image may be adopted. The store information includes one or more images. A description of the image is set for each image, such as the name, type, and features of the product included in the image. A recommendation level is set for each image, and is an index showing the degree to which the store recommends the image. A higher recommendation level indicates a stronger recommendation by the store.

The customer information storage unit 44 is a functional unit that stores customer information for each store. The customer information is information about users (hereinafter referred to as "customers") who actually visited a store in accordance with the recommendation information. As shown in FIG. 7, the customer information includes a store ID, bias information, and a behavior change rate. The bias information is information indicating the psychological tendencies of all customers of the store indicated by the store ID, and includes a bias value for each psychological tendency. Each bias value is, for example, the average value of the bias values of the psychological tendencies of all customers. The behavior change rate is set for each recommended store, and indicates the proportion of behavior change among all customers of the store indicated by the store ID. Each behavior change rate is, for example, the average value of the behavior change rates of all customers.

The acquisition unit 31 is a functional unit that acquires various information. For example, the acquisition unit 31 acquires information indicating a terminal ID, user basic information, and questionnaire responses from the mobile terminal 10 of a new user. For example, the acquisition unit 31 acquires a terminal ID and location information from the mobile terminal 10. For example, the acquisition unit 31 acquires store information from a store terminal 20. The acquisition unit 31 outputs the store information to the store information storage unit 43 and stores it in the store information storage unit 43.

The analysis unit 32 is a functional unit that analyzes the psychological tendency of each of the multiple groups and generates group information for each of the multiple groups. The analysis unit 32 calculates a bias value for each psychological tendency for each of the multiple users based on information indicating the questionnaire responses obtained from the mobile terminal 10. For each group, the analysis unit 32 calculates the average bias value of the users belonging to the group for each psychological tendency, and sets this as the bias value of the psychological tendency of the group. In this manner, the analysis unit 32 generates group information. The analysis unit 32 outputs the group information to the group information storage unit 41 and stores it in the group information storage unit 41.

The setting unit 33 is a functional unit that sets the user information of the new user. The setting unit 33 sets the terminal ID and user basic information received from the new user's mobile terminal 10 to the terminal ID and user basic information of the new user. The setting unit 33 identifies a group to which the new user belongs from among multiple groups based on the user basic information of the new user, and sets the user information of the new user using the group information of the identified group. The method of setting the user information will be described in detail later. The setting unit 33 outputs the user information of the new user to the user information storage unit 42 and stores it in the user information storage unit 42.

The extraction unit 34 is a functional unit that extracts stores that may be recommended. The extraction unit 34 extracts stores that are located in the vicinity of the mobile terminal 10.

The determination unit 35 is a functional unit that determines a recommendation target based on bias information indicating the psychological tendency of the user. The determination unit 35 determines a recommendation target based on at least one of the bias values included in the bias information of the user information. In this embodiment, the determination unit 35 performs a first determination process that determines a recommendation target based on one bias value, and a second determination process that determines a recommendation target based on multiple bias values. Details of the first determination process and the second determination process will be described later.

The generation unit 36 is a functional unit that generates recommendation information related to a recommendation target. In this embodiment, the generation unit 36 generates recommendation information related to a store that is a recommendation target determined by the determination unit 35. The recommendation information includes an image related to the store. The image may be a product image of a product provided by the store. For example, the generation unit 36 selects an image for recommendation from among images included in the store information based on the recommendation degree of the image included in the store information of the store determined by the determination unit 35. If preference information is included in the user information, the generation unit 36 may select an image for recommendation further based on the preference information. The generation unit 36 generates recommendation information including an image for recommendation.

The generation unit 36 may generate recommendation information according to the psychological tendency of the user. For example, the generation unit 36 may generate a message according to the psychological tendency of the user, and generate recommendation information including a recommendation image and a message. The message is, for example, a message that induces a user to take action.

The output unit 37 is a functional unit that outputs recommendation information. The output unit 37 outputs (transmits) the recommendation information to the mobile terminal 10 together with an instruction to set the image included in the recommendation information to the mobile terminal 10 as a background image on the lock screen of the mobile terminal 10. When the recommendation information includes a message, the output unit 37 may further output an instruction to display the message superimposed on the background image.

The determination unit 38 is a functional unit that determines the user's behavior after the recommendation information is output to the mobile terminal 10. The determination unit 38 determines, for example, whether or not the user who owns the mobile terminal 10 has acted in accordance with the recommendation information.

The update unit 39 is a functional unit that updates the bias information according to the judgment result by the judgment unit 38. The update unit 39 updates at least one of the bias values included in the bias information according to the judgment result. If the user acts in accordance with the recommendation information, the update unit 39 changes the bias value using an increase rate so that the degree of the psychological tendency increases, and if the user does not act in accordance with the recommendation information, the update unit 39 changes the bias value using a decrease rate so that the degree of the psychological tendency decreases. The decrease rate is smaller than the increase rate.

In this embodiment, the update unit 39 increases the bias value when the user acts in accordance with the recommendation information, and decreases the bias value when the user does not act in accordance with the recommendation information. The bias value is updated within the range of 0 to 1. The update unit 39 switches the method of updating the bias value depending on whether the determination unit 35 has determined the store to be recommended by the first determination process or the second determination process. Details of the method of updating the bias value will be described later.

Next, the user information setting process performed by the information providing device 30 will be described with reference to FIG. 8. FIG. 8 is a flowchart showing in detail the user information setting process performed by the information providing device shown in FIG. 1. The process shown in FIG. 8 is started, for example, when a user uses a recommendation application for the first time, by inputting basic user information, responses to a questionnaire, etc.

As shown in FIG. 8, first, the acquisition unit 31 acquires (receives) the terminal ID, the user basic information, and information indicating the questionnaire responses from the mobile terminal 10 (step S11). Then, the acquisition unit 31 outputs the user basic information and the information indicating the questionnaire responses to the analysis unit 32, and outputs the terminal ID and the user basic information to the setting unit 33.

Next, the analysis unit 32 generates group information (step S12). The method of generating group information will be specifically described below. When the information indicating the user basic information and the answers to the questionnaire is received from the acquisition unit 31, the analysis unit 32 calculates a bias value for each psychological tendency based on the information indicating the answers to the questionnaire obtained from the mobile terminal 10. For example, the analysis unit 32 calculates a bias value for time preference such that the bias value of time preference is larger as the amount (number of options) of the answer A1 shown in FIG. 2 is larger. The analysis unit 32 calculates a bias value for loss aversion bias such that the bias value of loss aversion bias is larger as the number (number of options) of the answer A2 shown in FIG. 2 is larger. The analysis unit 32 calculates a bias value for conformity bias such that the bias value of conformity bias is larger as the number of options of the answer A3 shown in FIG. 2 is larger. The analysis unit 32 calculates bias values for other psychological tendencies in a similar manner.

Then, the analysis unit 32 identifies a group to which the user who owns the mobile terminal 10 belongs, based on the user basic information. For example, if the user is a 35-year-old man, the analysis unit 32 identifies group M3, which is a group of men in their 30s. Then, for the identified group, the analysis unit 32 calculates the average bias value of the users belonging to the group for each psychological tendency, and sets this as the bias value of the psychological tendency of the group. In this manner, the analysis unit 32 generates group information. Then, the analysis unit 32 outputs the group information to the group information storage unit 41, and stores it in the group information storage unit 41.

Then, the setting unit 33 sets the user information of the new user (step S13). Specifically, when the setting unit 33 receives the terminal ID and the user basic information from the acquisition unit 31, the setting unit 33 sets the terminal ID and the user basic information received from the acquisition unit 31 to the terminal ID and the user basic information of the user information of the mobile terminal 10. Then, the setting unit 33 identifies the group to which the user who owns the mobile terminal 10 belongs from among multiple groups based on the user basic information, and sets the bias information of the new user using the group information of the identified group. For example, if the user is a 35-year-old man, the setting unit 33 acquires the group information of group M3 from the group information stored in the group information storage unit 41, and sets the group bias information included in the group information of group M3 to the bias information of the user information of the user.

In this manner, the setting unit 33 sets (generates) the user information of the new user. Note that an invalid value is set for each behavioral change rate in the user information. The setting unit 33 then outputs the user information of the new user to the user information storage unit 42 and stores it in the user information storage unit 42. This completes the user information setting process.

The analysis unit 32 generates group information in advance based on information indicating survey responses obtained from multiple users (mobile terminals 10). Therefore, the analysis unit 32 does not need to generate (update) group information every time a new user uses a recommended application for the first time. In this case, step S12 is omitted.

Next, the information providing method performed by the information providing device 30 will be described with reference to Figs. 9, 10, and Figs. 11(a) to 11(c). Fig. 9 is a flowchart showing a series of processes of the information providing method performed by the information providing device shown in Fig. 1. Fig. 10 is a flowchart showing the decision process shown in Fig. 9 in detail. Fig. 11(a) is a diagram showing an example of a lock screen on a mobile terminal. Fig. 11(b) is a diagram showing another example of a lock screen on a mobile terminal. Fig. 11(c) is a diagram showing yet another example of a lock screen on a mobile terminal. The series of processes shown in Fig. 9 is started, for example, by the user starting a recommended application. The mobile terminal 10 running the recommended application periodically transmits a terminal ID and location information to the information providing device 30.

As shown in FIG. 9, first, the acquisition unit 31 acquires the terminal ID and location information of the mobile terminal 10 (step S21). In this embodiment, information on the installation location of the access point is used as the location information of the mobile terminal 10. Then, the acquisition unit 31 outputs the terminal ID and location information to the extraction unit 34.

Then, the extraction unit 34 extracts stores (step S22). Specifically, when the extraction unit 34 receives the terminal ID and location information (the installation location of the access point) from the acquisition unit 31, the extraction unit 34 acquires a plurality of pieces of store information including access point information indicating the installation location of the access point from the plurality of pieces of store information stored in the store information storage unit 43. In other words, the stores of the acquired store information are extracted. Then, the extraction unit 34 outputs the terminal ID and the store information of the extracted plurality of stores to the determination unit 35.

Next, when the determination unit 35 receives the terminal ID and the store information of the extracted multiple stores from the extraction unit 34, the determination unit 35 performs a determination process to determine a store to be recommended from the extracted multiple stores (step S23). In the determination process of step S23, as shown in FIG. 10, the determination unit 35 first selects a candidate store to which customers will be sent based on the store information of the extracted multiple stores (step S31). Specifically, the determination unit 35 selects a candidate store to which customers will be sent based on the congestion level included in the store information of the extracted multiple stores. The determination unit 35 compares the congestion level with a preset first congestion threshold, and selects a candidate store with a congestion level less than the first congestion threshold as a candidate store to which customers will be sent.

Next, the determination unit 35 acquires user information including the terminal ID received from the extraction unit 34 from the multiple pieces of user information stored in the user information storage unit 42, and acquires customer information including the store ID of the candidate store selected in step S31 from the multiple pieces of customer information stored in the customer information storage unit 44 (step S32).

Then, the decision unit 35 judges whether or not to perform the first decision process (step S33). For example, if the first decision process has been performed consecutively in the previous information provision method performed for the user, and not all psychological tendency bias values have been selected in these consecutive first decision processes, the decision unit 35 judges to perform the first decision process (step S33; YES). Alternatively, if the second decision process has been performed in the previous information provision method performed for the user, and the second decision process has been performed a predetermined number of times consecutively in the previous information provision method, the decision unit 35 judges to perform the first decision process (step S33; YES).

In the first determination process, the determination unit 35 selects one bias value from the bias values of the multiple psychological tendencies included in the user information (step S34). Each time step S34 is performed, the bias values are selected in order so that the bias values of all psychological tendencies are selected.

On the other hand, if the first determination process has been performed consecutively in the previous information provision method performed for the user, and all of the bias values of the psychological tendencies have been selected in the consecutive first determination processes, the determination unit 35 determines to perform the second determination process (step S33; NO). Alternatively, if the second determination process has been performed in the previous information provision method performed for the user, but the second determination process has not been performed a predetermined number of times consecutively in the previous information provision method, the determination unit 35 determines to perform the second determination process (step S33; NO). In the second determination process, the determination unit 35 selects all of the bias values of the multiple psychological tendencies included in the user information (step S35). Note that the number of bias values selected in the second determination process may be greater than the number of bias values selected in the first determination process. In the second determination process, any bias value of two or more may be selected.

Then, the determination unit 35 calculates the similarity between each candidate store using the bias value selected in step S34 or step S35 (step S36). Specifically, the determination unit 35 generates a user vector having as elements the bias value selected in step S34 or step S35 and the behavior change rate included in the user information. Furthermore, the determination unit 35 generates a store vector for the visitor information of each candidate store having as elements the bias value of the psychological tendency corresponding to the bias value selected in step S34 or step S35 and the behavior change rate included in the visitor information. Then, the determination unit 35 calculates the similarity between the user vector and the store vector of each candidate store. Cosine similarity may be used as the similarity. In this case, the determination unit 35 normalizes each vector and calculates the cosine similarity using the normalized vector.

Then, the determination unit 35 determines the store to be recommended based on the similarity of each candidate store (step S37). For example, the determination unit 35 determines the candidate store having the greatest similarity among the candidate stores as the store to be recommended. In order to disperse the congestion level of the entire store, the determination unit 35 may determine the candidate store that satisfies the condition that the similarity is greater than a preset similarity threshold and the congestion level is less than a second congestion threshold as the store to be recommended. The second congestion threshold is less than the first congestion threshold.

When multiple candidate stores satisfy the above conditions, the determination unit 35 determines the candidate store with the greatest similarity among the candidate stores that satisfy the above conditions as the store to be recommended. When there is no candidate store that satisfies the above conditions, the determination unit 35 determines the candidate store with the greatest similarity or the candidate store with the least congestion level among the candidate stores to which customers will be sent as the store to be recommended. Then, the determination unit 35 outputs store information of the store to be recommended together with user information to the generation unit 36.

A method for determining stores to be recommended will be explained using a specific example. Assume that stores A, B, C, and D have been extracted, and the user information includes a bias value for time preference and a bias value for synchronization bias. The congestion level of store A is 0.3, the congestion level of store B is 0.4, the congestion level of store C is 0.7, and the congestion level of store D is 1.1. For example, when the first congestion threshold is set to 0.9, stores A, B, and C are selected as candidate stores to which customers are to be sent.

Then, customer information for store A, store B, and store C is obtained. For ease of explanation, the customer information is expressed as a store vector including the elements [time preference bias value, conformity bias bias value, behavior change rate for store A, behavior change rate for store B, behavior change rate for store C]. The store vector for store A is [0.8, 0.2, 0.6, 0.0, 0.1], the store vector for store B is [0.9, 0.6, 0.5, 0.2, 0.0], and the store vector for store C is [0.2, 0.5, 0.2, 0.1, 0.5]. Meanwhile, the user vector of the user information corresponding to the store vector is [0.8, 0.7, 0.4, 0.1, 0.1].

When the first determination process is performed and the bias value of time preference is selected, the similarity (cosine similarity) of each store is calculated using a vector obtained by removing the bias value of the synchronization bias from each of the above vectors. On the other hand, when the second determination process is performed, the similarity (cosine similarity) of each store is calculated using the above vectors. In this case, the similarity of store A is 0.878, the similarity of store B is 0.984, and the similarity of store C is 0.739. When the similarity threshold is set to 0.8 and the second congestion threshold is set to 0.7, stores A and B satisfy the conditions that the similarity is greater than the similarity threshold and the congestion level is less than the second congestion threshold, so store B, which has the higher similarity between stores A and B, is determined to be the store to be recommended.

Then, the generation unit 36 generates recommendation information (step S24). In step S24, when the generation unit 36 receives the store information and the user information from the determination unit 35, the generation unit 36 selects an image for recommendation from among the images included in the store information based on the recommendation level of the image included in the store information and the preference information included in the user information.

The generating unit 36 assigns a weight to each recommendation level based on, for example, the preference information. For example, the generating unit 36 assigns a weight greater than the default value to the recommendation level of an image that corresponds to a favorite item set in the preference information among images included in the store information. The default value is, for example, 1. The generating unit 36 assigns a default weight to the recommendation levels of other images. The generating unit 36 corrects the recommendation level by multiplying the recommendation level of each image by the weight. If a disliked item is set in the preference information, the generating unit 36 may exclude images of the disliked item (for example, a weight of 0). The generating unit 36 may exclude images that have been selected as recommended images in the past (for example, a weight of 0).

Then, the generation unit 36 uses the corrected recommendation level to select an image for recommendation from among the images included in the store information. The generation unit 36 may select an image with the highest recommendation level as the image for recommendation. The generation unit 36 may randomly select an image for recommendation from among images with a recommendation level equal to or higher than a preset recommendation threshold.

A method for selecting an image for recommendation will be described using a specific example. Assume that "seafood bowl" is set as a favorite item in the preference information, and "beef bowl" is set as a dislike item in the preference information. Assume that the images included in the store information are [beef bowl, new product, seafood bowl, char siu bowl], each recommendation level is [0.5, 0.9, 0.7, 0.4], and the weight assigned to the favorite items is set to 1.5. In this case, the preference information gives [beef bowl, new product, seafood bowl, char siu bowl] = [0, 0.9, 1.05, 0.4]. The generation unit 36 then selects the image of the seafood bowl with the highest recommendation level as the image for recommendation. If the recommendation threshold is set to 0.7, the generation unit 36 may select either the image of the new product or the image of the seafood bowl as the image for recommendation.

Furthermore, the generating unit 36 determines the psychological tendency with the maximum bias value among the bias values included in the user information. Then, the generating unit 36 generates a message according to the determined psychological tendency. For example, if the psychological tendency is time preference, the generating unit 36 generates a message indicating that the time can be shortened. An example of such a message is "You can enter the popular xx without waiting now!". If the psychological tendency is conformity bias, the generating unit 36 generates a message indicating that a large number of people are visiting. Examples of such a message are "When you think of Osaka, you think of xx!" and "Why not try the popular xx?". If the psychological tendency is loss aversion bias, the generating unit 36 generates a message indicating that a loss can be avoided or a profit can be obtained. Examples of such a message are "We are currently distributing xx coupons" and "If you go to xx, you will receive d points!". Note that if the maximum bias value is smaller than a predetermined threshold value (e.g., 0.5), the generating unit 36 generates the store name as a message.

Then, the generation unit 36 generates recommendation information including a recommendation image and message, and outputs the recommendation information to the output unit 37.

Then, the output unit 37 outputs the recommendation information (step S25). Specifically, when the output unit 37 receives the recommendation information from the generation unit 36, the output unit 37 outputs (transmits) the recommendation information to the mobile terminal 10 together with a command to set the image included in the recommendation information to the mobile terminal 10 as a background image on the lock screen of the mobile terminal 10. If the recommendation information includes a message, the output unit 37 further outputs a command to display the message superimposed on the background image. The output unit 37 may output the message by push communication. Furthermore, the output unit 37 may register the recommendation information in a history (log information) of the recommendation information (not shown).

When the mobile terminal 10 receives recommendation information from the information providing device 30, it sets the image included in the recommendation information as the background image on the lock screen, and displays the message included in the recommendation information superimposed on the background image. FIG. 11(a) shows an example of recommendation information displayed on the mobile terminal 10 of a user who has a time preference. FIG. 11(b) shows an example of recommendation information displayed on the mobile terminal 10 of a user who has a conformity bias. FIG. 11(c) shows an example of recommendation information displayed on the mobile terminal 10 of a user who does not have a specific psychological tendency. As shown in FIG. 11(a) to (c), recommendations are made according to the user's psychological tendency.

Then, the determination unit 38 determines the user's behavior after the recommendation information is output to the mobile terminal 10 (step S26). Specifically, the determination unit 38 determines whether the user who owns the mobile terminal 10 has acted according to the recommendation information. For example, the determination unit 38 acquires time-series location information of the mobile terminal 10 from the mobile terminal 10 via the acquisition unit 31 until a predetermined time has elapsed since the recommendation information was transmitted, and determines whether the user has acted according to the recommendation information based on the time-series location information. For example, if the location indicated by the location information remains within the recommended store for a certain period of time, the determination unit 38 determines that the user has acted according to the recommendation information, and otherwise determines that the user has not acted according to the recommendation information. Then, the determination unit 38 outputs a determination result indicating whether the user has acted according to the recommendation information to the update unit 39. Furthermore, the determination unit 38 registers the determination result in the history (log information) of the recommendation information (not shown).

Then, the update unit 39 updates the user information (step S27). In step S27, when the update unit 39 receives the judgment result, it updates the behavior change rate and bias value included in the user information according to the judgment result.

The update of the behavior change rate will be described in detail. The update unit 39 acquires the number of times that the recommendation information for the recommended store determined in step S23 was sent (number of transmissions) and the number of times that the user acted according to the recommended information (number of actions) from the recommendation information history (log information) not shown. The update unit 39 then calculates the behavior change rate of the recommended store by dividing the number of actions by the number of transmissions, and updates the behavior change rate of the store in the user information stored in the user information storage unit 42.

The method of updating the bias value differs depending on whether the store to be recommended is determined by the first determination process in step S23 or the second determination process. If the store to be recommended is determined by the first determination process, the update unit 39 updates only the bias value selected in step S34. More specifically, if the store to be recommended is determined by the first determination process and the user acts in accordance with the recommendation information, the update unit 39 increases the bias value selected in step S34. For example, the update unit 39 calculates an increase value by multiplying a value obtained by subtracting the current bias value from 1 (maximum value) by the increase rate, and updates (increases) the bias value by adding the increase value to the current bias value.

When a store to be recommended is determined by the first determination process and the user does not act in accordance with the recommendation information, the update unit 39 decreases the bias value selected in step S34. For example, the update unit 39 calculates the decrease value by multiplying the current bias value by the decrease rate, and updates (decreases) the bias value by subtracting the decrease value from the current bias value. The decrease rate is smaller than the increase rate.

For example, assume that a store to be recommended to a user who owns a mobile terminal 10 with terminal ID "T1" shown in FIG. 5 is determined using time preference. The increase rate is set to 0.1, and the decrease rate is set to 0.01. If the user acts in accordance with the recommendation information, the bias value of the time preference is changed from 0.6 to 0.64 (= 0.6 + (1 - 0.6) x 0.1). If the user does not act in accordance with the recommendation information, the bias value of the time preference is changed from 0.6 to 0.594 (= 0.6 - 0.6 x 0.01).

When the recommended store is determined by the second determination process, the update unit 39 updates all bias values based on the importance set for each bias value (psychological tendency). The importance is an index that indicates the degree of influence that each explanatory variable has on the objective variable when each bias value (psychological tendency) is an explanatory variable and the behavioral change rate is an objective variable. Each importance is calculated and set in advance by regression analysis or the like.

Specifically, when a store to be recommended is determined by the second determination process and the user acts in accordance with the recommendation information, the update unit 39 increases all bias values. For example, for each bias value, the update unit 39 calculates an increase value by multiplying a value obtained by subtracting the current bias value from 1 (maximum value), the increase rate, and the importance, and updates (increases) the bias value by adding the increase value to the current bias value.

The update unit 39 decreases all bias values when a recommended store is determined by the second determination process and the user does not act in accordance with the recommendation information. For example, the update unit 39 calculates a decrease value for each bias value by multiplying the current bias value, the decrease rate, and the importance, and updates (decreases) the bias value by subtracting the decrease value from the current bias value.

For example, assume that recommended stores are determined for a user who owns a mobile terminal 10 with terminal ID "T2" shown in Figure 5 using time preference and ownership bias. The increase rate is set to 0.1, and the decrease rate is set to 0.01. The importance of time preference is set to 0.6, and the importance of ownership bias is set to 0.4. If the user acts in accordance with the recommendation information, the bias value of time preference changes from 0.8 to 0.812 (= 0.8 + (1 - 0.8) x 0.6 x 0.1), and the bias value of ownership bias changes from 0.5 to 0.52 (= 0.5 + (1 - 0.5) x 0.4 x 0.1). If the user does not act on the recommendation information, the time preference bias value is changed from 0.8 to 0.7952 (= 0.8 - 0.8 x 0.6 x 0.01), and the retention bias value is changed from 0.5 to 0.498 (= 0.5 - 0.5 x 0.4 x 0.01).

When the determination result indicates that the user has acted in accordance with the recommendation information and the user has visited the recommended store for the first time, the update unit 39 updates the visitor information of the recommended store. Specifically, the update unit 39 recalculates the bias value and behavior change rate of the visitor information, including the bias value and behavior change rate of the user. Note that the update unit 39 may update each bias value of the group bias information included in the group information in response to changing each bias value of the bias information included in the user information. Since fluctuations in the user's bias value have little effect on the group's bias value, the update unit 39 may update the group information periodically (about once a month), for example.

This completes the series of processes in the information provision method. The conditions for switching between the first and second determination processes in step S33 are set appropriately. As described above, the first determination process may be repeated until all psychological tendency bias values included in the user information are selected, after which the second determination process may be performed a predetermined number of times, and then the first determination process may be performed again.

In the information providing device 30 described above, recommendation information including an image related to a recommended store according to the psychological tendency of the user is generated, and the recommendation information is output to the mobile terminal 10 together with a command for setting the image to the mobile terminal 10 as a background image of the lock screen of the mobile terminal 10. Since the image related to the recommended store is set as a background image of the lock screen of the mobile terminal 10, the user can recognize the image related to the recommended store without any operation by the user. Furthermore, since the recommended store is determined according to the psychological tendency of the user, the possibility that the user will act according to the recommendation information can be increased. As a result, it is possible to improve the advertising effect. As a result, it is possible to improve the advertising effect. Furthermore, by using an image suitable for the background image of the lock screen rather than an image for blatant advertising, the image can be naturally viewed by the user.

For example, even among stores that sell the same type of product, some stores are familiar to the user, while others are popular stores with long waiting times. Some users are swayed by the majority opinion, while others try to avoid losses. For example, if a recommended store is determined according to the user's preferences without considering the user's psychological tendency, a store that offers food that the user likes may be determined as the recommended store. However, a user with a high time preference is unlikely to visit a store with a long waiting time. A user with a high status quo bias is unlikely to visit an unknown store even if it is popular. In the information providing device 30, recommendation information including an image of a recommended store according to the user's psychological tendency is generated, so that the possibility that the user will visit the recommended store can be increased.

The recommendation information further includes a message according to the psychological tendency of the user. The output unit 37 further outputs a command to display the message superimposed on the background image. With this configuration, even if the same image is set as the background image of the mobile terminals 10 of different users, the advertising effect can be improved by changing the message according to the psychological tendency of the user. As a result, the possibility that the user will visit the recommended store can be further increased. Since the image and the message are independent, the image does not become distorted.

When the bias value indicating the degree of the user's psychological tendency is smaller than a predetermined threshold, the generation unit 36 generates the name of the store to be recommended as a message. With this configuration, it is possible to avoid displaying a message with low advertising effectiveness.

The generation unit 36 selects a product image from among product images of products offered at the recommended store based on the recommendation level of the product, and generates recommendation information using the selected product image as an image for recommendation. With this configuration, it is possible to select an image to be set as the background image of the user's mobile terminal 10, taking into consideration the store's intentions, such as wanting to put the product on sale.

The generation unit 36 selects a product image as an image for recommendation based on the user's preference information. With this configuration, the image to be set as the background image of the user's mobile terminal 10 is selected with further consideration given to the user's preferences. Therefore, it is possible to further increase the possibility that the user will visit the recommended store.

Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the above embodiments.

The information providing device 30 may be configured as a single device that is physically or logically coupled, or may be configured as multiple devices that are physically or logically separated from each other. For example, the information providing device 30 may be realized by multiple computers distributed over a network, such as in cloud computing. As described above, the configuration of the information providing device 30 may include any configuration that can realize the functions of the information providing device 30.

The recommendation target is not limited to commercial facilities such as stores. The recommendation target may be a facility other than a commercial facility. The recommendation target may be a place, product, etc. that can be recommended to the user.

In the above embodiment, a store clerk (manager) sets the congestion level of the store via the store terminal 20. In this case, since there is a possibility that the store clerk will forget to set the congestion level, the default congestion level may be set to a value indicating congestion (e.g., 1.0). Since there is a possibility that the store clerk will forget to update the congestion level, the congestion level may be updated to a value indicating congestion (e.g., 1.0) if a certain amount of time has passed since the last update.

The information providing device 30 may calculate the degree of congestion of a store. For example, when the location indicated by the location information of the mobile terminal 10 is included within the store, the information providing device 30 determines that the user of the mobile terminal 10 has entered the store and estimates the number of users present in the store. The information providing device 30 may then calculate the degree of congestion by dividing the estimated number of people by the number of people the store can accommodate. When an access point is installed in the store, the information providing device 30 may calculate the degree of congestion by dividing the number of mobile terminals 10 captured by the access point by the result of multiplying the number of people the store can accommodate by the line share rate.

In the above embodiment, the group is defined by an attribute, but may be defined by a bias value. For example, a set of users having a time preference of 0.8 or more may be defined as a group. The group information may further include a behavior change rate. The behavior change rate of the group information is, for example, the average behavior change rate of the users belonging to the group. In this case, the determination unit 35 may determine the store to be recommended using the group information of the group to which the user belongs, instead of the user information.

In the above embodiment, the generation unit 36 selects an image for recommendation based on the recommendation level of the image included in the store information and the preference information included in the user information, but the generation unit 36 may select an image for recommendation based on the recommendation level of the image included in the store information without using the preference information. In this case, the generation unit 36 may select an image with the highest recommendation level as the image for recommendation. The generation unit 36 may randomly select an image for recommendation from among images with a recommendation level equal to or higher than a preset recommendation threshold.

In the above embodiment, if the similarity of all candidate stores is equal to or less than the similarity threshold, the candidate store with the greatest similarity is determined as the store to be recommended. However, since the similarity is equal to or less than the similarity threshold, the user is unlikely to visit the recommended store. Therefore, the determination unit 35 may determine as the store to be recommended a store that is different from the candidate stores and offers products in common with a store whose similarity is greater than the similarity threshold.

Specifically, the determination unit 35 calculates the similarity between the remaining stores that were not selected as candidate stores among the stores extracted by the extraction unit 34 and the user. Specifically, the determination unit 35 generates store vectors for the remaining stores in the same way as the candidate stores, and calculates the similarity between the user vector and the store vector. Then, when the similarity of the remaining stores is greater than the similarity threshold, the determination unit 35 determines the candidate stores that provide products similar to the products provided by the remaining stores as stores to be recommended. The determination unit 35 calculates the similarity between the description of the image included in the store information of the remaining stores and the description of the product included in the store information of each candidate store. The determination unit 35 calculates the similarity by, for example, pattern matching of character strings. Then, the determination unit 35 determines the candidate stores indicated by the store IDs included in the store information including the description of the product whose similarity is greater than a predetermined threshold as stores to be recommended. In this case, the generation unit 36 selects the image of the product whose similarity is greater than a predetermined threshold as the image for recommendation.

For example, suppose that stores A, B, and C are selected as candidate stores, and the similarity between all of the stores is about 0.1. Although the similarity between store D is 0.9, store D is not selected as a candidate store because its congestion level is greater than the first congestion threshold. The product descriptions in the store information for store D include [soy sauce ramen, miso ramen, salt ramen, char siu rice bowl, fried rice], and it is determined whether the store information for each candidate store has a product description similar to these product descriptions. If the product descriptions in the store information for store C include [beef bowl, new product, seafood bowl, char siu rice bowl], then since "char siu rice bowl" is common, store C is determined as the store to be recommended. Then, an image of the char siu rice bowl is selected as the image for recommendation.

In the above embodiment, the image included in the recommendation information is set as the background image of the lock screen of the mobile terminal 10, but it may also be set as the background image of the home screen. In this case, the output unit 37 outputs a command to the mobile terminal 10 to set the image included in the recommendation information as the background image of the home screen of the mobile terminal 10.

The determination unit 38 may estimate the location (store) visited by the user who owns the mobile terminal 10 after the recommendation information is output to the mobile terminal 10. The determination unit 38 estimates the location visited by the user from, for example, time-series location information of the mobile terminal 10. The update unit 39 may change the reduction rate according to the location visited by the user when a store to be recommended is determined by the first determination process and the user does not act in accordance with the recommendation information. For example, when the user visits a location that is contrary to the psychological tendency selected in the first determination process, the update unit 39 sets the reduction rate to a value larger than a default value (for example, 0.01). When the user visits a location close to the psychological tendency selected in the first determination process, the update unit 39 sets the reduction rate to a value smaller than the default value.

The analysis unit 32 may analyze the psychological tendency of each user and generate user information. In this case, the setting unit 33 outputs the user information generated by the analysis unit 32 to the user information storage unit 42 and stores it in the user information storage unit 42. Therefore, the information providing device 30 may not have a group information storage unit 41. The information providing device 30 may not further have the analysis unit 32, the setting unit 33, and the user information storage unit 42. In this case, the information providing device 30 may acquire user information from an external user information storage unit. Similarly, the information providing device 30 may not have a store information storage unit 43. In this case, the information providing device 30 may acquire store information from an external store information storage unit. Similarly, the information providing device 30 may not have a visitor information storage unit 44. In this case, the information providing device 30 may acquire visitor information from an external visitor information storage unit.

The method by which the generation unit 36 generates the recommendation information is not limited to the method in the above embodiment. It is sufficient that the generation unit 36 can generate recommendation information including an image related to the store to be recommended, and the information providing device 30 does not need to include the extraction unit 34 and the determination unit 35.

The user information and visitor information do not need to be updated. In this case, the information providing device 30 does not need to include the determination unit 38 and the update unit 39.

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. There are no particular limitations on the method of realizing each functional block. That is, each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional blocks may be realized by combining the one device or the multiple devices with software.

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

For example, the information providing device 30 in one embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure. FIG. 12 is a diagram showing an example of the hardware configuration of an information providing device according to one embodiment of the present disclosure. The information providing device 30 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, and a bus 1007.

In the following description, the term "apparatus" may be interpreted as a circuit, device, unit, etc. The hardware configuration of the information providing apparatus 30 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

The functions of the information providing device 30 are realized by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, and a register. For example, each function of the information providing device 30 described above may be realized by the processor 1001.

The processor 1001 reads out 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 according to these. The programs used are those that cause a computer to execute at least some of the operations described in the above-mentioned embodiments. For example, each function of the information providing device 30 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 be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunications line.

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

Storage 1003 is a computer-readable recording medium, and may be, for example, 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) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, and a magnetic strip. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

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 referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize at least one of, for example, Frequency Division Duplex (FDD) and Time Division Duplex (TDD). For example, the above-mentioned acquisition unit 31 and output unit 37, etc., may be realized by the communication device 1004.

The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one structure (e.g., a touch panel).

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.

The information providing device 30 may 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.

Notification of information is not limited to the aspects/embodiments described in this disclosure and may be performed using other methods.

The processing steps, sequences, flow charts, etc. of each aspect/embodiment described in this disclosure may be reordered unless inconsistent. 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.

Information, etc. may be output from a higher layer to a lower layer, or from a lower layer to a higher layer. Information, etc. may be input/output via multiple network nodes.

The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

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

Each aspect/embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of specific information (e.g., notification that "X is the case") is not limited to explicit notification, but may be performed implicitly (e.g., by not notifying the specific information).

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 as an illustrative example and does not have any limiting meaning on the present disclosure.

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.

Software, instructions, information, and the like may be transmitted and received via a transmission medium. For example, if the 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), these wired and/or wireless technologies are included within the definition of a transmission medium.

The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

In addition, the terms explained in this disclosure and the terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

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

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.

The names used for the parameters described above are not limiting in any way. Furthermore, the formulas etc. using these parameters may differ from the formulas etc. explicitly disclosed in this disclosure.

The terms "determining" and "determining" as used in this disclosure may encompass a wide variety of actions. "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" may include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), and the like. "Determining" may include resolving, selecting, choosing, establishing, comparing, and the like. In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." "Judgment (decision)" may also be interpreted as "assuming," "expecting," or "considering," etc.

The terms "connected" and "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 at least one of one or more wires, cables, and 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.

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."

Any reference to elements using designations such as "first" and "second" used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply either that only two elements may be employed or that the first element must precede the second element in any way.

The "part" in the configuration of each of the above devices may be replaced with a "circuit" or a "device", etc.

When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Additionally, the term "or," as used in this disclosure, is not intended to be an exclusive or.

In this disclosure, where articles have been added by translation, such as "a," "an," and "the" in English, this disclosure may include that the nouns following these articles are plural.

In this 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 "combined" may also be interpreted in the same way as "different."

1...information provision system, 10...mobile terminal (terminal device), 20...store terminal, 30...information provision device, 31...acquisition unit, 32...analysis unit, 33...setting unit, 34...extraction unit, 35...determination unit, 36...generation unit, 37...output unit, 38...determination unit, 39...update unit, 41...group information storage unit, 42...user information storage unit, 43...store information storage unit, 44...visitor information storage unit, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims (4)

A calculation unit that calculates a bias value of each item indicating a psychological tendency of each of a plurality of users;
a determination unit that generates a user vector having as elements at least one bias value among the bias values of a target user to whom recommendation information is to be provided and a behavioral change rate that is set for each candidate of the recommendation target and indicates the proportion of the target user who acted according to the recommendation information of the candidate, generates a candidate vector having as elements average values of the at least one bias value and the behavioral change rate of users who acted according to the recommendation information of the candidate, and determines the recommendation target from among the candidates based on the similarity between the user vector and the candidate vector;
A generation unit that generates the recommendation information including an image related to the recommendation target;
an output unit that outputs the recommendation information to the terminal device together with a command for setting the image as a background image of the terminal device of the target user;
Equipped with
The recommendation target is a commercial facility,
the generation unit corrects the recommendation degree by weighting the recommendation degree of each product provided at the commercial facility using preference information indicating what the target user likes, selects a product image from product images of the products provided at the commercial facility based on the corrected recommendation degree, and generates the recommendation information using the selected product image as the image;
The generation unit assigns a weight to the recommendation degree of the favorite product indicated by the preference information that is greater than the weight assigned to other products. The information providing device according to claim 1, wherein the command is a command for setting the image on the terminal device as a background image on a lock screen of the terminal device. The generation unit generates the recommendation information further including a message according to the psychological tendency of the target user,
The information providing device according to claim 1 , wherein the output unit further outputs a command for displaying the message superimposed on the background image. the generation unit selects an item having a maximum bias value among bias values of each item indicating a psychological tendency of the target user , and generates the message using a predetermined template for the selected item ;
The information providing device according to claim 3 , wherein the generating unit generates a store name as a message when the maximum bias value is smaller than a predetermined threshold value.

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