JP2022027154A - Information providing apparatus - Google Patents

Abstract

To provide a user with recommendation information which is suitable for the user.SOLUTION: An information providing apparatus 30 includes: a determination unit 35 which selects recommendation candidates associated with candidate information that satisfies a condition allowed by a user, out of store information being information on stores, on the basis of user information including characteristics of the user, and determines a recommendation object out of the recommendation candidates; a generation unit 36 which generates recommendation information related to the recommendation object; and an output unit 37 which outputs the recommendation information to a portable terminal of the user.SELECTED DRAWING: Figure 3

Description

The present disclosure relates to an information providing device.

Patent Document 1 describes a device that detects the degree of congestion of each store and provides point information indicating a store (point) whose degree of congestion is lower than the standard to a mobile terminal. The device determines a store whose degree of congestion is lower than the standard, and then provides point information regarding the store according to the user's preference.

Japanese Unexamined Patent Publication No. 2017-191500

According to the apparatus described in Patent Document 1, the user is not provided with the location information of the store whose congestion degree is equal to or higher than the standard. For example, even a user who is willing to wait until entering a store cannot obtain store location information if the degree of congestion is equal to or higher than the standard. Therefore, the device described in Patent Document 1 cannot provide the user with information about the facility suitable for the user.

It is an object of the present disclosure to provide an information providing device capable of providing a user with appropriate recommendation information for the user.

The information providing device according to one aspect of the present disclosure selects a recommendation candidate associated with candidate information that satisfies the conditions acceptable to the user among the candidate information that is information about the recommendation candidate, based on the user information including the characteristics of the user. It includes a determination unit that selects and determines a recommendation target from the recommendation candidates, a generation unit that generates recommendation information regarding the recommendation target, and an output unit that outputs the recommendation information to the user's terminal device.

In this information providing device, if the candidate information satisfies the conditions acceptable to the user based on the user information, the recommendation target can be determined from the recommendation candidates associated with the candidate information. Since the recommendation target is determined based on the user information including the characteristics of the user and the candidate information, and the recommendation information is output to the user's terminal device, it is possible to provide the user with information related to the recommendation target appropriate for the user.

According to the present disclosure, it is possible to provide the user with recommendation information appropriate for the user.

FIG. 1 is a schematic configuration diagram of an information providing system including an information providing device according to an embodiment. FIG. 2 is a diagram showing an example of a questionnaire. FIG. 3 is a block diagram showing a functional configuration of the information providing device shown in FIG. FIG. 4 is a diagram showing an example of group information. FIG. 5 is a diagram showing an example of user information. FIG. 6 is a diagram showing 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 apparatus shown in FIG. FIG. 9 is a flowchart showing a series of processes of the information providing method performed by the information providing apparatus shown in FIG. FIG. 10 is a flowchart showing the determination process shown in FIG. 9 in detail. FIG. 11A is a diagram showing an example of a lock screen in a mobile terminal. FIG. 11B is a diagram showing another example of the lock screen in the mobile terminal. FIG. 11 (c) is a diagram showing still another example of the lock screen in the mobile terminal. FIG. 12 is a diagram showing a hardware configuration of the information providing device shown in FIG.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same elements are designated by the same reference numerals, and duplicate description will be omitted.

A configuration of an information providing system including an information providing device according to an embodiment will be described with reference to FIG. 1. FIG. 1 is a schematic configuration diagram of an information providing system including an information providing device according to an embodiment. The information providing system 1 shown in FIG. 1 is a system that provides a user with recommendation information regarding a recommendation target based on the characteristics of the user. The recommendation target is a target that is recommended (recommended) by the user. The recommendation target is selected from the recommendation candidates. Recommendation candidates are targets that can be recommended (recommended) to users. Examples of recommended targets and recommended candidates include commercial facilities such as stores. In this embodiment, a store is used as a recommendation target and a recommendation candidate.

The user's characteristics include physical characteristics, psychological tendencies, abilities, or the user's unique characteristics expressed by combining these. The user characteristics of this embodiment refer to psychological tendencies. Psychological tendencies are also referred to as psychological characteristics, biases, and the like. Psychological tendencies are tendencies of thinking in human behavior and judgment. Examples of psychological tendencies include entrainment bias, loss avoidance bias, status quo bias, possession bias, time preference (current orientation bias), entrainment effect, risk preference, Concorde effect, anchoring effect, and naiveness.

Entrainment bias is a psychological tendency to be swept away by a large number of opinions. Loss avoidance bias is a psychological tendency to avoid losses. The status quo bias is a psychological tendency to create a psychological barrier to the unknown and stick to the current situation. Possession bias is a psychological tendency to find great value in what you have and feel reluctant to let go. Possession bias can also mean a psychological tendency to consider oneself to have something rare. Time preference is a psychological tendency to prefer the one closer to the present. Risk appetite is a psychological tendency to take risks. The entrainment effect is a psychological tendency to gain an advantage over others. The Concorde effect is a psychological tendency that makes it impossible to make rational judgments because of the desire not to waste the time and money spent so far. The anchoring effect is a psychological tendency in which judgment is distorted in the order of information obtained. Naiveness is a psychological tendency in which planned and actual behavior do not match.

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

The mobile terminal 10 is a device used by the user and can be carried by the user. Examples of the mobile terminal 10 include smartphones and tablet terminals. An application for receiving recommendation information (hereinafter referred to as "recommendation application") is installed in the mobile terminal 10. The user registers the user basic information when the recommended application is used (started) for the first time. Examples of basic user information include gender and age. The recommendation application conducts a questionnaire to the user for estimating the user's psychological tendency (psychological characteristics). The questionnaire is conducted, for example, following the registration of basic user information. The questionnaire prepares questions for estimating psychological tendencies and options for answering the questions for each psychological tendency.

As shown in Fig. 2, as a question Q1 for estimating time preference, for example, "You can get 10,000 yen now. If you wait for a week, you can get a little more. How much will you get after a week? If so, can you wait for a week? " As an option of answer A1 to question Q1, for example, "1) 10100 yen, 2) 10500 yen, 3) 11000 yen, 4) 12000 yen, 5) 12001 yen or more" is prepared. As a question Q2 for estimating the loss avoidance bias, for example, "Do you go to several tourist spots a day when you go on a trip?" Is prepared. As an option of answer A2 to question Q2, for example, "1) 1 place, 2) 2 places, 3) 3 places, 4) 4 places, 5) 5 places or more" are prepared. As a question Q3 for estimating the entrainment bias, for example, "Implement 〇 × for 10 people including you and raise your hand to the person who applies. I thought you were ×. How many other people If you raise your hand to 〇, will you be hung up and raise your hand to 〇? ”Is prepared. As an option of answer A3 to question Q3, for example, "1) raise a hand to x, 2) 9 people, 3) 8 people, 4) 7 people, 5) 6 people" are prepared.

Questions and answers are prepared for other biases as well. As a question for estimating the holding bias, for example, "How many branded goods do you own?" Is prepared. As options for answering this question, for example, "1) 0, 2) 1-2, 3) 3-4, 4) 5-6, 5) 7 or more" are prepared. As a question to estimate the entrainment effect, for example, "When you are working on content that competes with others, is it motivated to be in the top of the ranking? Please select the one that applies." ing. As an answer option to this question, for example, "1) You can enjoy even at the bottom of the ranking, 2) It is not important to enter the top of the ranking, but if you do, you want to enter, 3) Basically, you want to enter the top of the ranking. There is no doubt that 4) being in the top of the ranking will be a big motivation, and 5) being in the top of the ranking will be the most concentrated effort. "

The mobile terminal 10 transmits basic user information and information indicating an answer to a questionnaire 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 referred to as wallpaper.

The user may set the permissible congestion degree, the permissible cost, and the permissible distance as an example of the user information in the recommendation application. The permissible congestion degree is the degree of congestion of the store that the user can tolerate with respect to the store. The permissible cost is the cost at which the user can tolerate the payment to the store. The permissible distance is a distance at which the user can move to the store. The permissible congestion degree, permissible cost, and permissible distance may be set for each store type, store, or product. The goods include services (services), rights, information, and the like. The user can set the permissible congestion degree, the permissible cost, and the permissible distance at any time. For example, the user may set the permissible congestion degree, the permissible cost, and the permissible distance when the recommendation application is first used (launched). Depending on the permissible congestion degree, the permissible cost, and the permissible distance being set, the mobile terminal 10 transmits the permissible congestion degree, the permissible cost, and the permissible distance to the information providing device 30.

Further, the user may set preference information as an example of user information in the recommendation application. The preference information is information indicating the user's preference. For example, as preference information, what the user likes (for example, favorite food) is set. As the preference information, what the user dislikes (for example, disliked food) may be set. The user may set preference information by selecting favorite and disliked items from a list prepared in advance in the recommendation application. As the preference information, a larger numerical value may be assigned to a certain product or service as the preference increases. The maximum value of palatability is, for example, 1, and the minimum value of palatability is, for example, 0. The user can set preference information at any time. For example, the user may set preference information when the recommendation application is first used (launched). Depending on the preference information being set, the mobile terminal 10 transmits the preference information to the information providing device 30.

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

The store terminal 20 is a terminal device provided in each store and managing 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. The store clerk (manager) sets each information included in the store information at an arbitrary timing via the store terminal 20. The description of the image may be set by selecting from the explanations 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 has 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 apparatus 30 will be described with reference to FIG. FIG. 3 is a block diagram showing a functional configuration of the information providing device shown in FIG. As shown in FIG. 3, the information providing device 30 functionally has an acquisition unit 31, an analysis unit 32, a setting unit 33, an extraction unit 34, a determination unit 35, a generation unit 36, and an output. It includes a unit 37, 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 of 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 the group. The attribute information is information indicating the attribute that defines the group. In the example shown in FIG. 4, gender and age are used as attributes. The group bias information is information indicating the psychological tendency of the group, and includes the bias value of each psychological tendency. The bias value of the psychological tendency is a value indicating the degree of the psychological tendency. In the present embodiment, the larger the bias value, the stronger the psychological tendency. The minimum value of the bias value of the psychological tendency is 0, and the maximum value of the bias value of the psychological tendency is 1. The bias value of each psychological tendency included in the group bias information is, for example, the average value of the bias values of the psychological tendency of the users belonging to the group.

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

The behavior change rate of the user is provided for each store targeted for recommendation, and indicates the rate at which the user acts according to the recommendation information when the recommendation information is received. The behavior change rate for a store is, for example, the ratio of the number of times the recommendation information about the store is received to the number of times the recommendation information is sent to the store. That is, the behavior change rate for a store is obtained by dividing the number of times the person has acted according to the recommendation information about the store by the number of times the recommendation information about the store is received. Since the terminal ID, the user basic information, the preference information, the bias information, the behavior change rate, the permissible congestion degree, the permissible cost, and the permissible distance are associated with each other, the user basic information and the preference information , Bias information, behavior modification rate, permissible congestion degree, permissible cost, and permissible distance are information of the user who possesses the mobile terminal 10 identified by the terminal ID.

The store information storage unit 43 is a functional unit that stores (stores) store information (an example of candidate information) of each store. As shown in FIG. 6, the store information includes a store ID, a store name, a store location information (an example of candidate location information), a degree of congestion, a candidate cost, an access point information, an image, and an image. Includes description and recommendation level. The store ID is information that can uniquely identify the store. The store name is the name of the store. The store location information includes store location information (latitude and longitude). Store location information can be obtained using map information and the like. The degree of congestion is an index showing how crowded a store is. In the present embodiment, the larger the value of the degree of congestion, the more crowded it is. For example, if the number of people existing in the store is the same as the number of people that the store can accept (for example, the number of seats), the degree of congestion is 1.0. The candidate cost indicates the feature amount of the consideration paid for the goods or services at the store. The candidate cost is, for example, the average value of the consideration paid at the store. The access point information is information that can identify the access point located closest to the store. In FIG. 6, the place where the access point is installed is shown for convenience of explanation.

The image is an image about the store. An example of an image is a product image, which is an image of a product provided by a store. The image can be used as a background image of the mobile terminal 10. Therefore, an image suitable for the background image can be adopted. The store information includes one or more images. The description of the image is set for each image, and is, for example, the name of the product, the type of the product, the feature, and the like included in the image. The degree of recommendation is set for each image and is an index indicating the degree to which the store recommends the image. The higher the recommendation level, the stronger the recommendation by the store.

The store visitor information storage unit 44 is a functional unit that stores (stores) visitor information of each store. The store visitor information is information about a user who actually visited the store according to the recommendation information (hereinafter referred to as "visitor"). As shown in FIG. 7, the store visitor information includes the store ID, the bias information, and the behavior change rate. The bias information is information indicating the psychological tendency of all the visitors of the store indicated by the store ID, and includes the bias value of each psychological tendency. The bias value of each psychological tendency is, for example, the average value of the bias values of the psychological tendency of all visitors. The behavior change rate is provided for each recommended store, and indicates the rate at which all the visitors of the store indicated by the store ID change their behavior. Each behavior change rate is, for example, the average value of the behavior change rates of all visitors. The visitor information may include a bias value of the psychological tendency of the visitor who has changed the behavior by recommending the image for each image included in the store information of each store.

The acquisition unit 31 is a functional unit that acquires various types of information. The acquisition unit 31 acquires, for example, the terminal ID, the user basic information, and the information indicating the answer to the questionnaire from the mobile terminal 10 of the new user. The acquisition unit 31 acquires the terminal ID and the user position information from the mobile terminal 10, for example. The acquisition unit 31 acquires store information from the store terminal 20, for example. 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 plurality of groups and generates group information for each of the plurality of groups. The analysis unit 32 calculates the bias value of the psychological tendency for each of the plurality of users based on the information indicating the answer of the questionnaire obtained from the mobile terminal 10. For each group, the analysis unit 32 calculates the average value of the bias values of the psychological tendencies of the users belonging to the group for each psychological tendency, and uses it as the bias value of the psychological tendency of the group. As described above, the analysis unit 32 generates the 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 for setting user information of a new user. The setting unit 33 sets the terminal ID and the user basic information received from the new user's mobile terminal 10 in the new user's terminal ID and the user basic information. The setting unit 33 identifies a group to which the new user belongs from a plurality of groups based on the user basic information of the new user, and sets the user information of the new user by using the group information of the group. The details of the user information setting method will be described 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 can be recommended. The extraction unit 34 extracts stores existing in the vicinity of the mobile terminal 10.

The determination unit 35 is a functional unit that determines as a recommendation target a store associated with store information that satisfies the conditions acceptable to the user, based on the user information. The determination unit 35 determines the recommendation target based on at least one of the bias values of the psychological tendency included in the bias information of the user information. Specifically, the determination unit 35 selects the recommendation candidate based on the parameter set based on the bias value of the psychological tendency, and determines the recommendation target from the recommendation candidates. The parameters will be described later.

The determination unit 35 sets an index used for selecting a recommendation candidate from a plurality of stores extracted by the extraction unit 34 in the permissible congestion degree, the permissible cost, and the permissible distance based on the magnitude of the value of the above parameter. To decide from. The determination unit 35 has a first selection process for selecting recommendation candidates based on the allowable congestion level, a second selection process for selecting recommendation candidates based on the allowable cost, and a third selection process for selecting recommendation candidates based on the allowable distance. Perform at least one with the selection process. That is, the determination unit 35 may execute any one of the first selection process, the second selection process, and the third selection process, or may execute a plurality of selection processes in combination. The determination unit 35 selects a recommendation candidate from the store candidates by executing at least one of the first selection process, the second selection process, and the third selection process. The determination unit 35 determines the recommendation target from the recommendation candidates. The details of the first selection process, the second selection process, and the third selection process, and the details of the method of determining the recommendation target will be described later.

The generation unit 36 is a functional unit that generates recommendation information regarding the recommendation target. In the present embodiment, the generation unit 36 generates the recommendation information regarding the store that is the recommendation target determined by the determination unit 35. Recommendation information includes images about the store. The image may be a product image of a product provided by the store. The generation unit 36 generates recommendation information based on at least one of user information and store information. For example, the generation unit 36 selects a recommendation image from the images included in the store information based on the recommendation level of the image included in the store information of the store determined by the determination unit 35. When the user information includes preference information, the generation unit 36 may select an image for recommendation 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 may generate recommendation information including an image for recommendation and a message. The message is, for example, a message that induces a user's action.

The generation unit 36 may generate the recommendation information based on the visitor information (an example of the behavior information) of the visitor (user) who acted according to the recommendation information and the user information of the target user who provides the recommendation information. Details will be described later.

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 a command for setting the image included in the recommendation information in 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 a command for displaying the message superimposed on the background image.

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

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

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

Then, the analysis unit 32 identifies the group to which the user who owns the mobile terminal 10 belongs, based on the basic user 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 thirties. Then, for the specified group, the average value of the bias values of the psychological tendencies of the users belonging to the group is calculated for each psychological tendency, and the bias value of the psychological tendency of the group is used. As described above, the analysis unit 32 generates the 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.

Subsequently, 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 terminal ID and the user basic information received from the acquisition unit 31 are used as the terminal ID and the user basic information of the user information of the mobile terminal 10. Set to information. Then, the setting unit 33 identifies the group to which the user who owns the mobile terminal 10 belongs from a plurality of groups based on the user basic information, and sets the bias information of the new user by using the group information of the group. do. For example, when the user is a 35-year-old man, the setting unit 33 acquires the group information of the group M3 from the group information stored in the group information storage unit 41, and the group bias included in the group information of the group M3. Set the information to the bias information of the user information of the user.

As described above, the setting unit 33 sets (generates) the user information of the new user. An invalid value is set for each behavior change rate of the user information. Then, 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. This completes the user information setting process.

The analysis unit 32 generates group information in advance based on information indicating answers to questionnaires obtained from a plurality of users (mobile terminal 10). Therefore, the analysis unit 32 does not have to generate (update) the group information every time the new user first uses the recommendation application. In this case, step S12 is omitted.

Next, the information providing method performed by the information providing apparatus 30 will be described with reference to FIGS. 9, 10 and 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 apparatus shown in FIG. FIG. 10 is a flowchart showing the determination process shown in FIG. 9 in detail. FIG. 11A is a diagram showing an example of a lock screen in a mobile terminal. FIG. 11B is a diagram showing another example of the lock screen in the mobile terminal. FIG. 11 (c) is a diagram showing still another example of the lock screen in the mobile terminal. The series of processes shown in FIG. 9 is started, for example, by the user launching the recommendation application. The mobile terminal 10 running the recommendation application periodically transmits the terminal ID and the user position information to the information providing device 30.

As shown in FIG. 9, first, the acquisition unit 31 acquires the terminal ID and the user position information of the mobile terminal 10 (step S21). Then, the acquisition unit 31 outputs the terminal ID and the user position information to the extraction unit 34.

Subsequently, the extraction unit 34 extracts the store (step S22). Specifically, when the extraction unit 34 receives the terminal ID and the user position information from the acquisition unit 31, the extraction unit 34 uses the plurality of store information stored in the store information storage unit 43 to indicate the area around the point indicated by the user position information. Get multiple store information. That is, 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. When the user position information is the information of the installation position of the access point, the extraction unit 34 uses the access point information indicating the installation position of the access point from a plurality of store information stored in the store information storage unit 43. Acquire multiple store information including.

Subsequently, when the determination unit 35 receives the terminal ID and the store information of the extracted plurality of stores from the extraction unit 34, the determination unit 35 performs a determination process of determining the store to be recommended from the extracted plurality of stores (the determination unit 35). Step S23). In the determination process of step S23, as shown in FIG. 10, first, the determination unit 35 acquires user information and visitor information (step S31). The determination unit 35 acquires user information including the terminal ID received from the extraction unit 34 from a plurality of user information stored in the user information storage unit 42. The determination unit 35 acquires the store visitor information including the store ID in the store information received from the extraction unit 34 from the plurality of store visitor information stored in the store visitor information storage unit 44.

Subsequently, the determination unit 35 sets parameters for determining whether or not to execute the first selection process, the second selection process, and the third selection process based on the bias value of the psychological tendency included in the user information. (Step S32). The parameters include congestion parameters, cost parameters, and distance parameters. Congestion parameters are calculated using time preference, loss avoidance bias, and tuning bias. The value of the congestion degree parameter is, for example, the average value of the bias value of time preference, the bias value of loss avoidance bias, and the bias value of tuning bias. Cost parameters are calculated using loss avoidance bias and entrainment effect. The value of the cost parameter is, for example, the average value of the bias value of the loss avoidance bias and the bias value of the tuning effect. Distance parameters are calculated using time preference and loss avoidance bias. The value of the distance parameter is, for example, the average value of the bias value of the time preference and the bias value of the loss avoidance bias. Note that these parameters may be calculated based on the value of one bias or the value of four or more biases included in the user information.

Subsequently, the determination unit 35 determines whether or not to execute the first selection process, the second selection process, and the third selection process based on the set congestion degree parameter, cost parameter, and distance parameter (step S33). ). For example, when the value of the congestion degree parameter is the highest among the congestion degree parameter, the cost parameter, and the distance parameter, the determination unit 35 determines to execute the first selection process. When the value of the cost parameter is the highest, the determination unit 35 determines to execute the second selection process. When the value of the distance parameter is the highest, the determination unit 35 determines to execute the third selection process. When the values of two or more parameters are the same, the determination unit 35 determines to execute the selection process associated with any one of the parameters having the same value.

Subsequently, the determination unit 35 executes the selection process determined in step S33 (step S34). In the first selection process, the determination unit 35 uses the allowable congestion degree and the congestion degree of each store. Specifically, the determination unit 35 selects a store associated with the store information including the congestion degree less than the permissible congestion degree of the user from the extracted plurality of stores as a recommendation candidate. In the second selection process, the decision unit 35 uses the permissible cost and the candidate cost of each store. Specifically, the determination unit 35 selects a store associated with the store information including the candidate cost less than the user's permissible cost from the extracted plurality of stores as a recommendation candidate. In the third selection process, the determination unit 35 calculates the candidate distance based on the user position information and the store position information. The candidate distance is the distance from the user's position to the store's position. In the third selection process, the determination unit 35 uses the permissible distance and the candidate distance of each store. Specifically, the determination unit 35 selects a store associated with the store information whose candidate distance is less than the user's permissible distance from the extracted plurality of stores as a recommendation candidate.

The determination unit 35 may further execute an unexecuted process among the first selection process, the second selection process, and the third selection process. If there is no recommendation candidate in step S34, the determination unit 35 returns to step S33, determines a selection process other than the already determined selection process, and then performs the newly determined selection process in step S34 again. Execute.

Subsequently, the determination unit 35 selects at least one psychological tendency bias value from the plurality of psychological tendency bias values included in the user information (step S35).

Subsequently, the determination unit 35 calculates the similarity of each recommendation candidate for each recommendation candidate selected in step S34 by using the bias value of the psychological tendency selected in step S35 (step S36). .. Specifically, the determination unit 35 generates a user vector having the bias value of the psychological tendency selected in step S35 and the behavior change rate included in the user information as elements. Further, the determination unit 35 has the bias value of the psychological tendency corresponding to the bias selected in step S35 and the behavior change rate included in the visitor information as elements for the visitor information of each recommendation candidate. Generate a store vector. Then, the determination unit 35 calculates the degree of similarity between the user vector and the store vector of each recommendation candidate. As the similarity, cosine similarity may be used. 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 recommended store based on the similarity of each recommendation candidate (step S37). For example, the determination unit 35 determines the recommendation candidate having the maximum similarity from the recommendation candidates as the recommended store. Then, the determination unit 35 outputs the store information of the recommended store to the generation unit 36 together with the user information. When one recommendation candidate is selected by the selection process, the determination unit 35 may determine the recommendation candidate as the recommendation target store. In this case, it is not necessary to execute the parameter selection (step S35) and the calculation of the similarity (step S36).

A method of determining a store to be recommended will be described using a specific example. Store A, store B, store C, and store D are extracted, and the user information includes a bias value of time preference, a bias value of loss avoidance bias, a bias value of tuning bias, and a bias value of tuning effect. It is assumed that it is. The bias value of time preference is 0.8, the bias value of loss avoidance bias is 0.6, the bias value of tuning bias is 0.7, and the bias value of tuning effect is 0.6. At this time, the value of the congestion degree parameter is 0.7, the value of the cost parameter is 0.6, and the value of the distance parameter is 0.7. Since the value of the congestion degree parameter and the value of the distance parameter are the largest, when the congestion degree parameter is selected as one bias, the determination unit 35 executes the first selection process. For example, it is assumed that the allowable congestion level included in the user information is 0.9. The congestion degree of the store A is 0.3, the congestion degree of the store B is 0.4, the congestion degree of the store C is 0.7, and the congestion degree of the store D is 1.1. Since a store having a congestion degree equal to or less than the allowable congestion degree is selected as a recommendation candidate to be sent, the determination unit 35 selects the store A, the store B, and the store C.

Then, the visitor information of the store A, the store B, and the store C is acquired. For convenience of explanation, the visitor information is used as a store vector including [time preference bias value, tuning bias bias value, behavior change rate for store A, behavior change rate for store B, behavior change rate for store C] as elements. Be expressed. The store vector of store A is [0.8, 0.2, 0.6, 0.0, 0.1], and the store vector of store B is [0.9, 0.6, 0.5]. , 0.2, 0.0], and the store vector of store C is [0.2, 0.5, 0.2, 0.1, 0.5]. On the other hand, 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 bias value of time preference is selected in step S35, the similarity (cosine similarity) of each store is calculated using the vector obtained by removing the bias value of the tuning bias from each of the above vectors. In this case, the similarity of the store A is 0.878, the similarity of the store B is 0.984, and the similarity of the store C is 0.739. Of the stores A, B, and C, the store B having the highest degree of similarity is determined as the recommended store.

Subsequently, the generation unit 36 generates the 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 converts the store information into the store information based on the recommendation degree of the image included in the store information and the preference information included in the user information. Select an image for recommendation from the included images.

The generation unit 36 weights each recommendation level based on, for example, preference information. For example, the generation unit 36 assigns a weight larger than the default value to the recommendation degree of the image corresponding to the favorite image set in the preference information among the images included in the store information. The default value is, for example, 1. The generation unit 36 assigns weights of default values to the degree of recommendation of other images. The generation unit 36 corrects the recommendation degree by multiplying the recommendation degree and the weight of each image. When the preference information is set to be disliked, the generation unit 36 may exclude the image of the disliked one (for example, the weight is 0). The generation unit 36 may exclude an image selected as a recommended image in the past (for example, the weight is 0).

Then, the generation unit 36 selects a recommendation image from the images included in the store information by using the corrected recommendation degree. The generation unit 36 may select the image having the maximum recommendation degree as the image for recommendation. The generation unit 36 may randomly select an image for recommendation from images having a recommendation degree equal to or higher than a preset recommendation threshold.

A method of selecting a recommendation image will be described using a specific example. It is assumed that "seafood bowl" is set as a favorite of preference information, and "gyudon" is set as a dislike of preference information. The image included in the store information is [Gyudon, new product, seafood bowl, char siu bowl], and each recommendation level is [0.5, 0.9, 0.7, 0.4], which is a preference. It is assumed that the weight given to the thing is set to 1.5. In this case, [gyudon, new product, seafood bowl, char siu bowl] = [0, 0.9, 1.05, 0.4] can be obtained from the preference information. Then, the generation unit 36 selects the image of the seafood bowl having the highest degree of recommendation as the image for recommendation. When 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.

When the visitor information includes a bias value of the psychological tendency of the visitor who recommended the image and changed the behavior for each image included in the store information of each store, the generation unit 36 may perform the visitor's visit. Recommendation information may be generated based on the user information of the target user who provides the person information and the recommendation information. The generation unit 36 recommends an image included in the store information and acquires a bias value of the psychological tendency of a visitor who has changed his / her behavior. Then, the generation unit 36 generates a visitor vector for each image, which has the average value of each bias value of the psychological tendency of the visitor who has changed the behavior as an element. The generation unit 36 generates a user vector having each bias value of the user's psychological tendency as an element.

Then, the generation unit 36 calculates the degree of similarity between the visitor vector and the user vector of each image. As the similarity, cosine similarity may be used. In this case, the generation unit 36 normalizes each vector and calculates the cosine similarity using the normalized vector. The generation unit 36 multiplies the similarity of each image by the recommendation degree corresponding to the image. The image corresponding to the element with the maximum value by this processing is selected. The generation unit 36 may select an image corresponding to the element having the maximum similarity without multiplying the recommendation degree of the image included in the store information by the similarity.

A method of selecting a recommendation image when visitor information is used will be described using a specific example. It is assumed that the image included in the store information is [soy sauce ramen, miso ramen, salt ramen]. The cosine similarity between the bias value of the psychological tendency of the visitor and the bias value of the psychological tendency of the user for each image included in the store information was [0.5, 0.8, 0.2]. do. The cosine similarity is obtained in the same manner as the above-mentioned calculation method. When the recommendation level for [Shoyu ramen, Miso ramen, Shio ramen] in the image included in the store information is [1.0, 0.7, 0.4], multiply it with the cosine similarity [ Soy sauce ramen, miso ramen, salt ramen] = [0.5, 0.56, 0.08] can be obtained. Then, the generation unit 36 selects the image of miso ramen having the highest degree of recommendation as the image for recommendation.

Then, the generation unit 36 generates the recommendation information including the image for the recommendation, and outputs the recommendation information to the output unit 37. In addition, the generation unit 36 may generate a message according to the determined psychological tendency. The generation unit 36 determines the psychological tendency having the largest bias value among the bias values of the psychological tendency included in the user information. For example, if the psychological tendency is time preference, the generation unit 36 generates a message indicating that the time can be shortened. An example of such a message is "Popular xx can now enter the store without waiting!". When the psychological tendency is a tuning bias, the generation unit 36 generates a message indicating that a large number of people are visiting. Examples of such messages include "Osaka is xx!" And "Let's go to the topic xx". When the psychological tendency is a loss avoidance bias, the generation unit 36 generates a message indicating that the loss can be avoided or the profit can be obtained. Examples of such messages include "We are delivering xx coupons only now" and "If you go to xx, you will receive a d point gift!". When the bias value of the maximum psychological tendency is smaller than a predetermined threshold value (for example, 0.5), the generation unit 36 may generate the store name as a message. The generation unit 36 may generate recommendation information including an image and a message for recommendation, and may output the recommendation information to the output unit 37.

Subsequently, 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 receives the recommendation information together with a command for setting the image included in the recommendation information in the mobile terminal 10 as a background image on the lock screen of the mobile terminal 10. Is output (transmitted) to the mobile terminal 10. When the recommendation information includes a message, the output unit 37 further outputs a command for displaying the message superimposed on the background image. The output unit 37 may output a message by push communication. Further, the output unit 37 may register the recommendation information in the history (log information) of the recommendation information (not shown).

When the mobile terminal 10 receives the recommendation information from the information providing device 30, the mobile terminal 10 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. 11A shows an example of recommendation information displayed on the mobile terminal 10 of a user having a time preference. FIG. 11B shows an example of the recommendation information displayed on the mobile terminal 10 of the user having the tuning 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 FIGS. 11A to 11C, recommendations may be made according to the psychological tendency of the user.

As a result, a series of processes of the information providing method is completed. The first selection process, the second selection process, and the third selection process are performed by repeating the steps (step S34) of determining a plurality of selection processes in the above flowchart and determining the selection process (step S33). May be all done. For example, in step S33, the determination unit 35 may determine the order in which the first selection process, the second selection process, and the third selection process are executed in descending order of the congestion degree parameter, the cost parameter, and the distance parameter. The determination unit 35 does not have to decide to execute all of the first selection process, the second selection process, and the third selection process.

In the information providing device 30 described above, a store associated with store information that satisfies the conditions acceptable to the user based on the user information is selected as a recommendation candidate, and a recommendation target is determined from the recommendation candidates. The recommendation information regarding the recommendation target is generated by the generation unit 36, and the recommendation information is output to the user's mobile terminal 10 by the output unit 37. For example, some users try to avoid a crowded store, while others can visit a slightly crowded store. As in this example, acceptable conditions can vary from user to user. For example, if a recommendation candidate is selected by a threshold set uniformly for all users without considering the user's acceptable conditions, the stores with the user-acceptable conditions will be excluded from the recommendation candidates. , There is a possibility that appropriate recommendation information cannot be provided to the user. When recommendation candidates are selected by a threshold that is uniformly set for all users, stores with conditions that the user cannot tolerate may be included in the recommendation candidates. Even if the recommendation information about such a store is provided to the user, it is unlikely that the user will visit the store. In the information providing device 30 of the present embodiment, a store associated with store information satisfying the conditions acceptable to the user is selected as a recommendation candidate, and the recommendation target is determined from the recommendation candidates, so that the user is the recommendation target. You can increase the chances of visiting a store. Therefore, the information providing device 30 can provide the user with the recommendation information appropriate for the user.

The store information includes the congestion degree of the store, the acceptable condition includes the allowable congestion degree which is the allowable congestion degree of the user, and the determination unit 35 includes the store information including the congestion degree less than the allowable congestion degree of the user. Select the associated recommendation candidates. For example, some users are willing to wait until they enter the store, while others want to avoid waiting. If only stores below the threshold of congestion that are uniformly set for all users are selected as recommendation candidates, stores that are more crowded than the threshold are excluded from the recommendation candidates, so users who are willing to wait. May not be provided with appropriate recommendation information. According to the above configuration, recommendation candidates are selected according to the degree of congestion that the user can tolerate. For example, when the permissible congestion level of the user is larger than the threshold value as compared with the case of recommending stores below the threshold value of the congestion level set uniformly in advance, even if the store has a congestion level of the threshold value or higher, the relevant store is concerned. Stores can be included in the recommendation candidates. Therefore, the possibility that the store is determined as the recommendation target increases, the possibility that the user visits the recommended store can be further increased, and the possibility that the store increases sales can be increased. Unlike the above example, even when the permissible congestion degree of the user is equal to or less than the above-mentioned congestion degree threshold value, it is possible to provide information on the store having an appropriate congestion degree for the user, and it takes the user to enter the store. It is possible to reduce the psychological burden such as stress due to time.

The store information includes candidate costs, which are costs related to the subject provided by the store, acceptable conditions include permissible costs, which are costs that the user can afford to pay, and the decision unit 35 is a candidate less than the user's permissible cost. Select recommended candidates associated with store information including costs. For example, some users may be willing to spend a relatively high amount of money in a store, while others may want to keep the cost low. If only stores below the cost threshold set uniformly for all users are selected as recommendation candidates, stores with higher costs than the threshold are excluded from the recommendation candidates, so users who do not mind the high cost May not be provided with appropriate recommendation information. According to the above configuration, recommendation candidates are selected according to the degree of congestion that the user can tolerate. For example, if the user's permissible cost is larger than the threshold value compared to the case of recommending a store that is less than the preset cost threshold value, the cost expected to be consumed at the store is higher than the threshold value. Even if there is, the store can be included in the recommendation candidates. Therefore, the possibility that the store is determined as the recommendation target increases, the possibility that the user visits the recommended store can be further increased, and the possibility that the store increases sales can be increased. Further, in the above example, since the number of recommendation candidates is large, it is possible to increase the possibility of sending the user to many stores. Unlike the above example, even if the user's permissible cost is below the above cost threshold, it is possible to provide information about the store at an appropriate cost for the user, reducing an unexpected financial burden on the user. Can be made to.

Further, the acquisition unit 31 for acquiring the user position information which is the user's position information is further provided, the candidate information includes the candidate position information which is the position information of the recommendation candidate, and the acceptable condition is the distance at which the user can move. Including a certain permissible distance, the determination unit 35 selects a recommendation candidate whose candidate distance, which is the distance from the user to the store calculated based on the user position information and the store position information, is less than the user's permissible distance. For example, some users may be relatively far from the store, while others may want the store to be nearby. If only stores with a distance less than the threshold value uniformly set for all users are selected as recommendation candidates, stores with a distance greater than the threshold value are excluded from the recommendation candidates, so users who do not care about the distance are acceptable. May not be provided with appropriate recommendation information. According to the above configuration, the recommendation candidate is selected according to the distance that the user can tolerate. For example, when the user's permissible distance is larger than the threshold value compared to the case of recommending a store with a distance less than the threshold value uniformly set in advance, the store is far away even if the distance is greater than or equal to the threshold value. Can be included in the recommendation candidates. Therefore, the possibility that the store is determined as the recommendation target increases, the possibility that the user visits the recommended store can be further increased, and the possibility that the store increases sales can be increased. Further, when the permissible distance of the user is larger than the above threshold value, the possibility of sending the user to a wide range can be increased. Unlike the above example, even when the user's permissible distance is less than or equal to the above distance threshold, it is possible to provide information about the store at an appropriate distance for the user, reducing an unexpected physical burden on the user. Can be made to.

The generation unit 36 generates recommendation information based on at least one of user information and store information. According to this configuration, for example, the recommendation information (image) provided to the user's mobile terminal 10 is selected in consideration of the user's preference included in the user information or the recommendation level of the store included in the store information. .. For example, when the recommendation information is generated based on the user information, the recommendation information is selected according to the user's preference, so that the possibility that the user visits the recommended store that matches the preference can be further increased. For example, when the recommendation information is generated based on the store information, the store can advertise the recommendation information with a high degree of recommendation to the user by selecting the recommendation information with a high degree of recommendation of the store.

The generation unit generates the recommendation information based on the visitor information of the visitor who acted according to the recommendation information and the user information of the target user who provides the recommendation information. According to this configuration, for example, when the visitor information includes a bias value of the psychological tendency of the visitor who behaves according to the recommendation information and the user information includes the bias value of the psychological tendency of the user as in the present embodiment. The recommendation information provided to the user's mobile terminal 10 is selected in consideration of each bias value. For example, the higher the similarity between the bias value of the psychological tendency of the visitor who acted according to the recommendation information and the bias value of the psychological tendency of the user, the more the user behaves according to the recommendation information in the same manner as the visitor who acted according to the recommendation information. It is thought that there is a high possibility of doing so. Therefore, even if the user information does not include the preference information related to the recommendation information, the user can select the recommended store by providing the recommendation information that leads to the visit of a store with a high degree of similarity. You can further increase your chances of visiting.

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 by one device physically or logically coupled, or may be configured by a plurality of devices physically or logically separated from each other. For example, the information providing device 30 may be realized by a plurality of computers distributed on a network such as cloud computing. As described above, the configuration of the information providing device 30 may include any configuration that can realize the function of the information providing device 30.

Recommendations are 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 that can be recommended to the user, a product, or the like.

In the above embodiment, the store clerk (manager) sets the store congestion degree and the candidate cost via the store terminal 20. In this case, since the clerk may forget to set the congestion degree and the candidate cost, the default value of the congestion degree may be set to a value indicating congestion (for example, 1.0), and the default value of the candidate cost is It may be set to a value indicating the cost (for example, 1000). Since the clerk may forget to update the congestion degree, the congestion degree may be updated to a value indicating congestion (for example, 1.0) when a certain time has passed since the previous update.

The information providing device 30 may calculate the degree of congestion in the store. For example, when the position indicated by the position information of the mobile terminal 10 is included in the store, the information providing device 30 determines that the user of the mobile terminal 10 has entered the store, and the number of users existing in the store. To estimate. Then, the information providing device 30 may calculate the degree of congestion by dividing the estimated number of people by the number of people that the store can accept. When the access point is installed in the store, the information providing device 30 divides the number of mobile terminals 10 captured by the access point by the result of multiplying the acceptable number of people in the store by the line share rate. The degree of congestion may be calculated accordingly.

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

At least one of the permissible congestion, the permissible cost, and the permissible distance may not be set in the recommendation application. For example, group information may include permissible congestion, permissible costs, and permissible distances. The setting unit 33 sets an index (either the allowable congestion degree, the allowable cost, or the allowable distance) that is not set by the user among the allowable congestion degree, the allowable cost, and the allowable distance into the group information of the group to which the user belongs. It may be set using the included indicators (permissible congestion, permissible cost, and permissible distance). The permissible congestion degree may be set based on the value of the congestion degree parameter. The permissible cost may be set based on the value of the cost parameter. The allowable distance may be set based on the value of the distance parameter.

The determination unit 35 does not have to select the first selection process, the second selection process, and the third selection process. In this case, the determination unit 35 selects a store associated with store information corresponding to at least one condition such as less than the allowable congestion degree, less than the allowable cost, and less than the allowable distance as a recommendation candidate.

In the above embodiment, the generation unit 36 selects the 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 selects the image for recommendation. The image for recommendation may be selected based on the degree of recommendation of the image included in the store information without using the preference information. In this case, the generation unit 36 may select the image having the maximum recommendation degree as the recommended image. The generation unit 36 may randomly select an image for recommendation from images having a recommendation degree equal to or higher than a preset recommendation threshold.

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 be set as the background image of the home screen. In this case, the output unit 37 outputs a command for setting the image included in the recommendation information in the mobile terminal 10 as a background image on the home screen of the mobile terminal 10.

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 does not have to include the group information storage unit 41. The information providing device 30 may not further include an analysis unit 32, a setting unit 33, and a 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 does not have to include the 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 does not have to include the 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 information providing device 30 does not have to include the extraction unit 34. In this case, the information providing device 30 may acquire all store information from the store information storage unit 43 or an external store information storage unit.

The block diagram used in the description of the above embodiment shows a block of functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one physically or logically coupled device, or two or more physically or logically separated devices can be directly or indirectly (eg, for example). , Wired, wireless, etc.) and may be realized using these plurality of devices. The functional block may be realized by combining the software with the one device or the plurality of devices.

Functions include judgment, decision, judgment, calculation, calculation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, solution, selection, selection, establishment, comparison, assumption, expectation, and assumption. These include broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assigning. Not limited to functions. For example, a functional block (constituent unit) that causes transmission to function is referred to as a transmitting unit or a transmitter. In each case, as described above, the realization method is not particularly limited.

For example, the information providing device 30 in one embodiment of the present disclosure may function as a computer that processes the present disclosure. FIG. 12 is a diagram showing an example of the hardware configuration of the information providing device according to the embodiment of the present disclosure. The above-mentioned information providing device 30 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.

In the following description, the word "device" can be read as a circuit, a device, a unit, or the like. The hardware configuration of the information providing device 30 may be configured to include one or more of the devices shown in the figure, or may be configured not to include some of the devices.

For each function in the information providing device 30, by loading predetermined software (program) on hardware such as the processor 1001 and the memory 1002, the processor 1001 performs an operation, controls communication by the communication device 1004, and a memory. It is realized by controlling at least one of reading and writing of data in 1002 and storage 1003.

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

The processor 1001 reads a program (program code), a software module, data, and the like from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these. As the program, a program that causes a computer to execute at least a part of the operations described in the above-described embodiment is used. For example, each function of the information providing device 30 may be realized by a control program stored in the memory 1002 and operating in the processor 1001. Although it has been described that the various processes described above are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. Processor 1001 may be mounted by one or more chips. The program may be transmitted from the network via a telecommunication line.

The memory 1002 is a computer-readable recording medium, for example, by at least one of ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), and the like. It may be configured. The memory 1002 may be referred to as a register, a cache, a main memory (main storage device), or the like. The memory 1002 can store a program (program code), a software module, or the like that can be executed to implement the information providing method according to the embodiment of the present disclosure.

The storage 1003 is a computer-readable recording medium, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, an optical magnetic disk (for example, a compact disk, a digital versatile disk, a Blu-ray). It may consist of at least one such as a (registered trademark) disk), a smart card, a flash memory (eg, a card, stick, key drive), a floppy (registered trademark) disk, and a magnetic strip. The storage 1003 may be referred to as an auxiliary storage device. The storage medium described above may be, for example, a database, server, or other suitable medium containing 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 includes, for example, a high frequency switch, a duplexer, a filter, a frequency synthesizer, and the like in order to realize at least one of FDD (Frequency Division Duplex) and Time Division Duplex (TDD). It may be composed of. For example, the above-mentioned acquisition unit 31, output unit 37, and the like may be realized by the communication device 1004.

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 an input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may have an integrated configuration (for example, 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 by using a single bus, or may be configured by using a different bus for each device.

The information providing device 30 includes hardware such as a microprocessor, a digital signal processor (DSP: Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array). The hardware may implement some or all of each functional block. For example, processor 1001 may be implemented using at least one of these hardware.

The notification of information is not limited to the embodiments / embodiments described in the present disclosure, and may be performed by other methods.

The processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in the present disclosure may be rearranged in order as long as there is no contradiction. For example, the methods described in the present disclosure present elements of various steps using exemplary order, and are not limited to the particular order presented.

Information and the like can be output from the upper layer to the lower layer, or from the lower layer to the upper layer. Information and the like may be input / output via a plurality of network nodes.

The input / output information and the like may be stored in a specific place (for example, a memory), or may be managed using a management table. Information to be input / output may be overwritten, updated, or added. The output information and the like may be deleted. The input information or the like may be transmitted to another device.

The determination may be made by a value represented by 1 bit (0 or 1), by a boolean value (Boolean: true or false), or by comparing numerical values (for example, a predetermined value). It may be done by comparison with the value).

The embodiments / embodiments described in the present disclosure may be used alone, in combination, or switched with each other in practice. Notification of predetermined information (for example, notification of "being X") is not limited to explicit notification, and may be implicitly (for example, by not notifying the predetermined 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 in the present disclosure. The present disclosure may be implemented as amendments and modifications without departing from the spirit and scope of the present disclosure as determined by the description of the scope of claims. Therefore, the description of the present disclosure is for purposes of illustration and has no limiting meaning to the present disclosure.

Software, whether called software, firmware, middleware, microcode, hardware description language, or other names, instructions, instruction sets, codes, code segments, program codes, programs, subprograms, software modules. , Applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, features, etc. should be broadly interpreted.

Software, instructions, information and the like may be transmitted and received via a transmission medium. For example, a website that uses at least one of wired technology (coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.) and wireless technology (infrared, microwave, etc.). When transmitted from a server or other remote source, at least one of these wired and wireless technologies is included within the definition of transmission medium.

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

The terms described in the present disclosure and the terms necessary for understanding the present disclosure may be replaced with terms having the same or similar meanings.

The terms "system" and "network" used in this disclosure are used interchangeably.

The information, parameters, etc. described in the present disclosure may be expressed using absolute values, relative values from a predetermined value, or other corresponding information. It may be represented.

The names used for the parameters mentioned above are not limiting in any way. Further, the mathematical formulas and the like using these parameters may differ from the mathematical formulas and the like explicitly disclosed in the present disclosure.

The terms "determining" and "determining" as used in this disclosure may include a wide variety of actions. "Judgment" and "decision" are, for example, judgment (judging), calculation (calculating), calculation (computing), processing (processing), derivation (deriving), investigation (investigating), search (looking up, search, inquiry). It may include (eg, searching in a table, database or another data structure), ascertaining as "judgment" or "decision". "Judgment" and "decision" are receiving (for example, receiving information), transmitting (for example, transmitting information), input (input), output (output), and accessing (accessing). ) (For example, accessing the data in the memory) may be regarded as "judgment" or "decision". "Judgment" and "decision" may include resolving, selecting, choosing, establishing, comparing, etc. as being regarded as "judgment" and "decision". .. That is, "judgment" and "decision" may include considering some action as "judgment" and "decision". "Judgment (decision)" may be read as "assuming", "expecting", "considering", or the like.

The terms "connected", "coupled", or any variation thereof, mean any direct or indirect connection or connection between two or more elements and each other. It can include the presence of one or more intermediate elements between two "connected" or "combined" elements. The connection or connection between the elements may be performed physically, logically, or may be realized by a combination thereof. For example, "connection" may be read as "access". As used in the present disclosure, the two elements use at least one of one or more wires, cables and printed electrical connections, and, as some non-limiting and non-comprehensive examples, radio frequencies. It can be considered to be "connected" or "coupled" to each other using electromagnetic energy having wavelengths in the region, microwave region and light (both visible and invisible) regions.

The phrase "based on" as used in this disclosure does not mean "based on" unless otherwise stated. In other words, the statement "based on" means both "based only" and "at least based on".

Any reference to elements using designations such as "first" and "second" as used in the present disclosure does not generally limit the quantity or order of those elements. These designations can be used in the present disclosure as a convenient way to distinguish between two or more elements. Therefore, references to the first and second elements do not mean that only two elements can be adopted and that the first element must somehow precede the second element.

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

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

In the present disclosure, if articles are added by translation, for example, "a", "an" and "the" in English, the present disclosure states that the nouns following these articles are plural. May include.

In the present disclosure, the term "A and B are different" may mean "A and B are different from each other". The term may mean that "A and B are different from C". Terms such as "separate" and "combined" may be interpreted in the same way as "different".

1 ... Information providing system, 10 ... Mobile terminal (terminal device), 20 ... Store terminal, 30 ... Information providing device, 31 ... Acquisition unit, 32 ... Analysis unit, 33 ... Setting unit, 34 ... Extraction unit, 35 ... Decision unit , 36 ... Generation unit, 37 ... Output unit, 41 ... Group information storage unit, 42 ... User information storage unit, 43 ... Store information storage unit, 44 ... Store visitor information storage unit, 1001 ... Processor, 1002 ... Memory, 1003 ... Storage, 1004 ... communication device, 1005 ... input device, 1006 ... output device, 1007 ... bus.

Claims (6)

Based on the user information including the characteristics of the user, the recommendation candidate associated with the candidate information satisfying the conditions acceptable to the user is selected from the candidate information which is the information about the recommendation candidate, and the recommendation is selected from the recommendation candidates. The decision-making part that decides the target, and
A generation unit that generates recommendation information regarding the recommendation target, and
An output unit that outputs the recommendation information to the user's terminal device, and
An information providing device. The candidate information includes the degree of congestion of the recommendation candidate.
The acceptable condition includes an acceptable degree of congestion, which is an acceptable degree of congestion for the user.
The information providing device according to claim 1, wherein the determination unit selects the recommendation candidate associated with the candidate information including the congestion degree less than the allowable congestion degree. The candidate information includes a candidate cost which is a cost related to the object provided by the recommendation candidate.
The acceptable conditions include an acceptable cost, which is the cost that the user can afford to pay.
The information providing device according to claim 1 or 2, wherein the determination unit selects the recommendation candidate associated with the candidate information including the candidate cost less than the allowable cost. Further, an acquisition unit for acquiring user position information, which is the user's position information, is provided.
The candidate information includes candidate position information which is the position information of the recommendation candidate.
The acceptable condition includes an allowable distance which is an allowable distance for the user to move.
The determination unit selects the recommendation candidate whose candidate distance, which is the distance from the user to the recommendation candidate calculated based on the user position information and the candidate position information, is less than the allowable distance. The information providing device according to any one of claims 3. The information providing device according to any one of claims 1 to 4, wherein the generation unit generates the recommendation information based on at least one of the user information and the candidate information. Any of claims 1 to 5, wherein the generation unit generates the recommendation information based on the behavior information of the user who acted according to the recommendation information and the user information of the user who is the target for providing the recommendation information. The information providing device described in item 1.

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