Method and program

Abstract

To provide a technology for making a new suggestion based on the taste of a user.SOLUTION: A method includes: acquiring user information including SNS basic information and MaaS basic information and position information from a first user terminal; acquiring, from an SNS account, user taste information including one of SNS posting character, image, tag, comment, URL link, follow, follower, and contents tagged with a function indicating sympathy or dislike feeling, based on the SNS basic information, or from a MaaS account, one of transportation means selection information, behavior information, moving pattern, reservation information, settlement information, time information, and moving means utilization rate data, based on the MaaS basic information; analyzing the taste of a user from the user taste information and item information; and outputting map information including an analysis result and the position information.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a method and a program for presenting and guiding recommended destinations. [Background technology]

[0002] Recommendation technology that suggests information that is likely to interest a user is known. In Patent Document 1, future events to be held at a place visited by the user can be introduced even after the user has left the place visited, so that the user can consider whether to visit the same destination again for a future event. In Patent Document 2, suggestions are made by estimating the user's preferences from the user's behavior history and internet search history, so that other actions that the user prefers can be estimated, and the possibility of making a suggestion that is meaningful to the user can be increased. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-138830 A [Patent Document 2] JP 2020-181427 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, when recommending destinations based on a user's preferences, such as previously visited places, behavioral history, and Internet search history, only similar destinations are suggested, making it difficult to suggest destinations that encourage new discoveries of preferences. One of the objectives of the present invention is to provide a technology that can make new suggestions based on a user's preferences. [Means for solving the problem]

[0005] A method according to one embodiment of the present invention includes acquiring user information including SNS basic information or MaaS basic information and location information from a first user terminal, acquiring user preference information including any one of SNS post text, images, tags, comments, URL links, following, followers, and content tagged with a function indicating sympathy or dislike from an SNS account based on the SNS basic information, or any one of transportation selection information, behavioral history, movement patterns, reservation information, payment information, time information, and transportation usage rate data from a MaaS account based on the MaaS basic information, analyzing the user preferences from the user preference information and item information, and outputting map information including the analysis results and location information.

[0006] The analysis results may include further prioritization.

[0007] The method may further include obtaining neighborhood information from the location information, and the item information may include the neighborhood information.

[0008] The method may further include acquiring store information from a second user terminal, and the item information may include the store information.

[0009] The store information may include basic SNS information, and may further include obtaining real-time information from an SNS account.

[0010] This may further include notifying a second user terminal of the analysis result and the user information.

[0011] The method may further include notifying a second user terminal when the analysis result is selected.

[0012] The item information may include event information.

[0013] The method may further include providing a route when an analysis result is selected.

[0014] The item information may include a web information page where destinations are aggregated.

[0015] A program for causing a computer to execute the method described above. Effect of the Invention

[0016] According to an embodiment of the present invention, new suggestions can be made based on the user's preferences. In particular, the suggestions are not limited to the user's previously visited places, behavioral history, internet search history, etc., so that the suggestions can be made to encourage the user to discover new preferences. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a diagram illustrating an overview of a recommender system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating the hardware configuration of a server and a mobile terminal according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram illustrating an overview of a program according to an embodiment of the present invention. [Figure 4] 2 is a flow chart illustrating a method according to one embodiment of the present invention. [Diagram 5] 2 is a flow chart illustrating a method according to one embodiment of the present invention. [Figure 6] 2 is a flow chart illustrating a method according to one embodiment of the present invention. [Figure 7] 2 is a flow chart illustrating a method according to one embodiment of the present invention. [Figure 8] 4 is a flowchart illustrating a program flow according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Hereinafter, the embodiments of the present invention will be described with reference to the drawings. However, the present invention can be implemented in many different forms, and should not be interpreted as being limited to the description of the embodiments exemplified below. In this specification and each figure, elements similar to those described above with reference to the previous figures are given the same reference numerals, and detailed descriptions may be omitted as appropriate.

[0019] [Recommender system configuration] An overview of a recommender system 1 according to an embodiment of the invention will be described. FIG. 1 is a schematic diagram of a recommender system 1 according to an embodiment of the present invention. The recommender system 1 is composed of a server 10 and a mobile terminal 20. The server 10 and the mobile terminal 20 are connected to each other via a network 30. There may be a plurality of servers 10 and mobile terminals 20. When there are a plurality of servers 10, the servers 10 may be connected to each other via the network 30.

[0020] The server 10 transmits and receives data to and from the mobile terminal 20. The server 10 acquires user information from the mobile terminal 20 on which an application program for providing information that is likely to interest the user is installed. The server 10 analyzes the user's preferences based on the user information and item information using AI, and transmits the analysis results to the mobile terminal 20. The server 10 transmits and receives data, processing requests, and the like to and from the mobile terminal 20 using an API (Application Programming Interface).

[0021] The mobile terminal 20 is, for example, an electronic device such as a smartphone or a mobile electronic terminal such as a wearable terminal.

[0022] [Server hardware configuration] 2A is a diagram showing a hardware configuration 100 of a server. The server 10 includes a control unit 101, a memory 102, a storage unit 103, and a communication unit 104.

[0023] The control unit 101 is, for example, a CPU (Central Processing Unit) or a quantum computer, and executes a program. For example, the control unit 101 reads out a program recorded in the storage unit 103, loads it into the memory 102, and executes it.

[0024] The storage unit 103 is, for example, a recording device capable of permanently retaining information and rewriting information, such as a non-volatile memory, a hard disk drive (HDD), or a solid state drive (SSD). The recording medium stores information such as a program and parameters required for the operation of the program. When there are multiple servers 10, the server 10 may be connected to the other servers 10 and used as, for example, a database.

[0025] The memory 102 is, for example, a rewritable storage device used for temporary information storage such as a volatile memory. The memory 102 is used as a development area for the program recorded in the storage unit 103 and as a storage area for storing intermediate data output during the operation of the program.

[0026] The communication unit 104 is a communication interface. In this embodiment, the server 10 connects to the mobile terminal 20 via the network 30 through the communication unit 104, and transmits and receives data to and from the mobile terminal 20. When there are multiple servers 10, the server 10 may connect to other servers 10 via the network 30 through the communication unit 104, and transmit and receive data to and from the other servers 10.

[0027] [Mobile device hardware configuration] 2B is a diagram showing a hardware configuration 200 of the mobile terminal 20. The mobile terminal 20 includes a control unit 201, a memory 202, a storage unit 203, a communication unit 204, a display unit 205, and an operation unit 206.

[0028] The control unit 201 is, for example, a CPU or a quantum computer, and executes a program. The control unit 201 reads out a program recorded in the storage unit 203, expands it in the memory 202, and executes it. The communication unit 204 transmits and receives data to and from the server 10.

[0029] The display unit 205 is a liquid crystal display, an EL display, a projector, or the like. The operation unit 206 may be operation keys, buttons, or the like, or may be a touch sensor capable of detecting touch coordinates on a display, voice operation, or image recognition instead of the operation keys or buttons. When the operation unit 206 is a touch sensor, the input operation is realized, for example, by a touch sensor and a software key controlled by a program. When the operation unit 206 is an image recognition unit, the input operation is realized, for example, by identifying gestures or sign language by image recognition of a camera, and operating or instructing.

[0030] [Program Structure] FIG. 3 is a diagram showing the program configuration of the server 10 and the mobile terminal 20. As shown in FIG.

[0031] In the server 10, a program for providing information that is likely to interest the user includes an input section 11, an AI section 12, and an output section 13.

[0032] The input unit 11 acquires user information from the user's mobile terminal 20. The user information may include, for example, a user profile. The user profile may be, for example, initial settings within an application that the user inputs to the mobile terminal 20, and may include gender, age, address, occupation, initial preference information, payment registration, and the like. The initial preference information may be, for example, nudge initial setting information, which means that the user himself can set the initial settings for behavior selection in a nudge, and the user may be able to change the settings as appropriate.

[0033] The user information may further include user preference information. The user preference information may be SNS basic information, MaaS basic information, behavioral history (browsing history), net information, future information, sound information, music app information, biometric information, recommended information from others, and the like. The SNS basic information and MaaS basic information may be information necessary for ID linkage of SNS or MaaS, and may be information obtained from SNS and MaaS accounts. The user preference information obtained from the SNS account may be, for example, SNS posted characters, images, tags, comments, URL links, and the like, and may be content tagged with functions indicating intentions such as following, following, empathy, or dislike. The user preference information obtained from the MaaS account may be, for example, the user's transportation means selection information, behavioral history, movement patterns, reservation information, payment information, time information, and transportation means usage rate data. The behavioral history may be, for example, location information, GPS history, navigation search words, browsing history (item information, time, etc. that the user viewed with interest before the behavior), and time zone information thereof. Examples of the internet information include the user's internet search history, SNS posted text, images, and voice, and the time period of those, calendar information, and weather information. Examples of the future information include the user's list of places to go (favorites). Examples of the sound information include, for example, the user's voice, surrounding environmental sounds, and music, and information on emotions, locations, and situations. For example, when a person with a speech disorder uses sound information, even if he or she cannot speak, he or she can use the conversations of people around him or her and environmental sounds as input information. Examples of the music app information include, for example, the history of music apps. Examples of the biometric information include, for example, body temperature, heart rate, number of steps, stress value, and sleep time input to the mobile terminal 20. Examples of the recommended information from others include, for example, recommended information from people, stores, companies, and the like that are followed. If there is a companion, the input unit 11 may further acquire user information of the companion. The input unit 11 transmits the acquired user information to the AI ​​unit 12.

[0034] The input unit 11 further acquires item information. The item information may include, for example, store and facility information, item information, and event information. The item information may be periodically acquired from an Internet site, and may be, for example, a web information page in which surrounding information, tourist information, event information, and destinations are aggregated. The store and facility information may be, for example, initial settings in an application input from a mobile terminal 20 such as a store or facility, and may be a category, location, business hours, SNS basic information, and the like. The SNS basic information may be information required for ID linkage of an SNS such as a store or facility, or may be information obtained from an SNS account. The item information and event information may be, for example, real-time information such as event information, information on freshly made products, and discount information registered and transmitted from a mobile terminal 20 such as a store or facility. The real-time information may be information transmitted on an SNS site such as a store or facility. The item information may also be store, facility information, item information, and event information obtained from a MaaS site of a user. In addition, the item information may be extracted by linking with a server such as an SNS or MaaS application to extract store, facility information, item information, and event information transmitted on an SNS or MaaS site.

[0035] The AI ​​unit 12 uses AI to analyze the user's preferences from the user information and item information acquired by the input unit 11. For example, by using SNS posted characters and images obtained from an SNS account as user information, it becomes possible to extract preference information that is not limited by the user's behavioral history, etc., and the accuracy of the preference information is improved. In addition, it becomes possible to extract negative information, and it can be used as a filter for preference information. For example, by using transportation mode selection information, behavioral history, movement patterns, reservation information, payment information, time information, and transportation mode usage rate data obtained from a MaaS account as user information, it becomes possible to extract preference information that matches the user's lifestyle habits and schedules, etc., and the accuracy of the preference information is improved. For example, by using real-time information as item information, it is possible to propose real-time information.

[0036] The output unit 13 outputs the analysis results analyzed by the AI ​​unit 12 to the user's mobile terminal 20. The analysis results may be, for example, static destinations, dynamic destinations, internet information, etc. Static destinations may be, for example, store information, etc. Dynamic destinations may be, for example, non-regular events, museum exhibition information, holiday event information, etc. Internet information may be, for example, event information posted on an event information site, etc. These analysis results may include location information indicating the location and time information indicating the opening and opening hours.

[0037] These analysis results may be further prioritized based on the user's preferences. Prioritization may be, for example, noise removal, ranking, etc. Noise removal may be, for example, removing information that does not match the user's preferences or does not match the context in which the user is currently placed. Ranking may be, for example, ranking information that is close to the user's preferences, and further, only the top ranked items may be displayed. The analysis results may be further classified by the user's customer demographics. The customer demographics may be, for example, categories such as age, gender, and hobby preferences. Hobby preference categories are categories of information that change from time to time, and can provide analysis results that are more suited to the user's preferences at that time.

[0038] On the other hand, the output unit 13 may output the analysis results analyzed by the AI ​​unit 12 together with user information, time information, location information, and the like to the mobile terminal 20 of the store or facility. By outputting the analysis results analyzed by the AI ​​unit 12, user information, time information, location information, and the like to the mobile terminal 20 of the store or facility, it is possible to suggest users whose preference information matches the store or facility. By obtaining information on users whose preference information matches, the store, facility, and the like can know when and what information to suggest to whom. Furthermore, further suggestions that match the customer's preferences can be sent, and the user's preferences can be reconciled with suggestions from the store, facility, and the like, and analysis results that are more suited to the user's preferences can be provided.

[0039] In the mobile terminal 20, a program for providing information that is likely to interest the user includes a providing unit 21, an input unit 22, and an output unit 23. However, without being limited to this, the mobile terminal 20 may also include an AI unit. The mobile terminal 20 may also include the above-mentioned input unit 11, AI unit 12, and output unit 13. In this case, the server 10 may not be provided.

[0040] The providing unit 21 provides the display unit 205 of the mobile terminal 20 with map information and spatial information including the analysis results analyzed by the AI ​​unit 12. For example, in the case of a terminal such as VR / AR, the map information may be provided in spatial information in multiple dimensions. The map information may include the time required from the current location to the analysis results by setting a means of transportation. The map information including the analysis results may include, for example, destinations around the current location, a travel plan, a detour destination, a future destination, and suggestions from stores. The detour destination may be, for example, a recommended destination where a detour can be made on the way of the user's travel route. The detour destination may be, for example, a detour destination that takes into account congestion and congestion information of the destination store, and free time to avoid congestion. The future destination may be, for example, a proposal that allows the user to make a reservation for an action at a non-regular event such as an art exhibition, a market, or a square. The square may be an empty space or a parking lot in a park or a facility. Suggestions from a store may include, for example, advertisements, sale information, product arrival information, event information including guest information, congestion information, etc., and this information may be linked to the store's SNS or user-to-user SNS. Linking to user-to-user SNS allows even stores that do not use SNS to suggest more information. Linking to SNS as store suggestions allows notifications to be sent to targeted customers and real-time information to be suggested. Real-time information allows destinations to be suggested in a more efficient order based on time information, required time, congestion information, inventory information, etc. For example, when there is a large amount of inventory or when the expiration date is approaching, more proactive suggestions can contribute to food waste prevention.

[0041] The input unit 22 is a software keyboard, touch panel, microphone, camera, AR glasses, various sensors (light, temperature, humidity, acceleration, gyro, pressure, vibration, distance), etc., which allow the user to confirm and select the analysis results analyzed by the AI ​​unit 12. The user can confirm and select a destination from the analysis results analyzed by the AI ​​unit 12. The user's confirmation and selection may be notified to the store with the user's consent. By notifying the store of the user's confirmation and selection, the store can understand what kind of customer is interested in what, and the user can communicate his or her own hobbies and preferences to the store.

[0042] The output unit 23 provides map information including the destination input by the user through the input unit 22 to the display unit 205 of the mobile terminal 20. The map information may include route guidance from the current location to the destination, and may also include the required time by setting the means of transportation. There may be multiple route guidance from the current location to the destination, and it may be provided in combination with congestion information and the required time. In addition, if the analysis result selected by the user is a future destination, the user can set it so that navigation automatically starts on the date and time of a future event by making an action reservation. By linking with a calendar, a reservation for a future destination can also be automatically set on the calendar. The user's action reservation may be notified to the store. By notifying the store of the user's action reservation, the store can know in advance what kind of customers will come, thereby improving the accuracy of marketing.

[0043] [How to suggest destinations] Next, a method for presenting recommended destinations according to an embodiment of the present invention will be described with reference to Fig. 4. The server 10 causes the control unit 101 to execute each process.

[0044] 4 is a flowchart illustrating a series of processing steps of a method according to an embodiment of the present invention. First, the input unit 11 of the server 10 acquires user information (a user profile including initial preference information of a user who has logged in to the system) from the storage unit 203 of the mobile terminal 20 (step S401). However, this is not limited to the above, and the user may input initial preference information when starting an application even if the user is not logged in to the system. In addition, the input unit 11 of the server 10 acquires a current location from the mobile terminal 20 (step S402).

[0045] The input unit 11 of the server 10 acquires surrounding area information from an Internet site based on the current location information acquired in step S402 (step S403).

[0046] The AI ​​unit 12 of the server 10 recommends information that matches the user's preferences from the surrounding information acquired in step S403 (step S404). For example, the AI ​​unit 12 recommends shops and places that match initial preference information such as liking bakeries, paintings, and the sea and mountains that allow a sense of nature.

[0047] Next, the input unit 11 of the server 10 acquires the user's action history from the mobile terminal 20 (step S405). Also, the input unit 11 of the server 10 acquires the user's favorites (user preference information) from the storage unit 203 of the mobile terminal 20 (step S406).

[0048] The AI ​​unit 12 of the server 10 analyzes the user's preferences from the user's behavior history acquired in step S405 and the favorites and item information acquired in step S406 (step S407).

[0049] The output unit 13 of the server 10 may prioritize the analysis results obtained in step S407 and extract only the top ranked shops (Top-N Shops for users) (step S408).

[0050] Furthermore, if there is a companion, the preferences of the companion may be analyzed using user information obtained from the portable terminal 20 of the companion in steps S401 to S407. The output unit 13 of the server 10 may optimize the ranking of the analysis result obtained in step S408 with the analysis result of the companion (step S409). The optimization may further narrow down the analysis result according to the conditions of the facility / store, the companion, the group, and the funds. The conditions of the facility / store may be, for example, the category, the age limit, the time limit, the location, the language, the number of people available, and the like. The conditions of the companion may be, for example, the constitution (allergy), the disability, the religion, the food culture (vegan, vegetarian, halal, and the like), the values, and the like. The conditions of the group may be, for example, the number of people, the means of transportation, and the like. The conditions of the funds may be, for example, the upper limit of the available funds, and the like.

[0051] The output unit 13 of the server 10 may generate a nudge UI (User Interface) that matches the user's preferences. The nudge UI may be, for example, a UI for each personal type, and if the personal type is an efficiency-oriented type, it may emphasize time efficiency, if the personal type is a text-oriented type, it may be pleasant text, if the personal type is a visual-oriented type, it may be good-looking images or videos, if the personal type is an anxious type, it may encourage early reservations, if the personal type is a contribution type, it may encourage contribution, if the personal type is a health-conscious type, it may emphasize calorie display, and if the personal type is a voice-oriented type, it may be a voice UI.

[0052] The output unit 13 of the server 10 provides the map information including the analysis results (optimum recommendation information) obtained in steps S407 to S409 to the mobile terminal 20 using the nudge UI generated in S410 (step S411).

[0053] Furthermore, the input unit 11 of the server 10 may periodically collect item information from an Internet site (step S412). The item information may be, for example, a web information page that aggregates destinations. The input unit 11 of the server 10 may calculate a similarity between the item information obtained in step S412 and the user's preferences (step S413). The input unit 11 of the server 10 may prioritize the item information based on the similarity between the item information obtained in step S412 and the user's preferences obtained in step S413, and extract only the top ranked items (Top-N Shop for shop) (step S414). The prioritized item information obtained in step S414 may be provided to step S408 and used to prioritize the user's analysis results.

[0054] [Method of analyzing user preferences using basic SNS information or MaaS basic information] Next, a method for analyzing user preferences using SNS basic information or MaaS basic information as user information will be described in detail. FIG. 5 is a flowchart for explaining a method for analyzing user preferences using SNS basic information or MaaS basic information. First, the user performs ID federation settings for SNS or MaaS on an application program of the mobile terminal 20 (first user terminal) (step S501). Examples of SNS include Instagram, Twitter, and Facebook. Examples of MaaS include Whim, moovel, and my route. However, the present invention is not limited to these, and ID federation settings may be performed for music distribution services such as Spotify, Apple Music, and Amazon Music, and ID federation settings may be performed for accounts such as Yahoo!, Google, Amazon, and Podcast services.

[0055] Next, the input unit 11 of the server 10 acquires user preference information from the SNS or MaaS account based on the SNS or MaaS ID information set in step S501 (step S502). The user preference information obtained from the SNS account may be, for example, SNS posted text, images, tags, comments, URL links, etc., and may be content tagged with functions indicating intentions such as following, followers, sympathy, or dislike. The user preference information obtained from the MaaS account may be, for example, the user's transportation means selection information, behavior history, movement patterns, reservation information, payment information, time information, and transportation means usage rate data.

[0056] The AI ​​unit 12 of the server 10 analyzes the user's preferences from the user preference information and item information acquired in step S502 (step S503). These analysis results may further include location information indicating a location, time information indicating a time, and the like.

[0057] The output unit 13 of the server 10 may chronologically sequence and rank the analysis results obtained in step S503 (step S504). Past user preference information may be stored.

[0058] The output unit 13 of the server 10 prioritizes (ranks) the analysis results obtained in steps S503 and S504, and provides them to the mobile terminal 20 together with the map information including the above (step S505).

[0059] [How to propose non-routine events] Next, a method for proposing a non-regular event will be described in detail. Fig. 6 is a flowchart for explaining a method for proposing a non-regular event. First, a store registers basic information about the store on an application program of the mobile terminal 20 (second user terminal) (step S601). Examples of the basic information about the store include categories.

[0060] Next, the store registers event information on the application program of the mobile terminal 20 (step S602). Event information includes, for example, the event name, location, time, category, and the like.

[0061] The input unit 11 of the server 10 acquires the event information registered in step S602, and publishes and announces it on the Internet site (step S603).

[0062] The AI ​​unit 12 of the server 10 narrows down the preference target users based on the analysis results of the user preferences acquired in steps S407 and S503 (step S604).

[0063] The output unit 13 of the server 10 notifies the target users narrowed down in step S604 of the event information (step S605). The event information may be, for example, the event information registered in step S602, or an image of the most recent event. Furthermore, the output unit 13 of the server 10 may notify the information of the target users narrowed down in step S604 to the mobile terminal 20 of the store that registered the event information. By notifying the store of the information of the target users, the store can know when and to whom to propose what information.

[0064] The user makes an action reservation based on the event information notification received in step S604 (step S606). The user's action reservation information may be notified to the mobile terminal 20 of the store. By notifying the store of the user's action reservation, the store can know in advance what kind of customers will be attending, thereby improving the accuracy of marketing. A discount coupon or the like may be attached to the event information notification, and the user may obtain the discount coupon or the like from the event information notification (step S607).

[0065] The output unit 13 of the server 10 provides the map information including the action reminder for the action reservation made by the user in step S606 to the mobile terminal 20 (step S608).

[0066] The input unit 11 of the server 10 may collect the user's event usage record from the mobile terminal 20 (step S609). At this time, the input unit 11 of the server 10 may collect usage records such as points (points calculated under certain conditions according to the purchase amounts of various products and services or the number of visits to the store, etc.) together with the event usage record.

[0067] The input unit 11 of the server 10 may update the user's behavior history and the store performance information (step S610).

[0068] [How to provide real-time information] Next, a method for proposing real-time information will be described in detail. Fig. 7 is a flowchart for explaining a method for proposing real-time information. First, a store or facility registers basic information about the store or facility on an application program of the mobile terminal 20 (step S701). Examples of basic information about a store include category, location, and business hours.

[0069] Next, the store or facility registers and transmits real-time information on the application program of the mobile terminal 20 (step S702). Real-time information includes, for example, information on events, freshly made products, discount information, and the like.

[0070] The input unit 11 of the server 10 acquires basic information and real-time information of the registered stores and facilities (step S703).

[0071] The user sets the location information of the user (terminal) on the application program of the mobile terminal 20 (step S704).

[0072] The input unit 11 of the server 10 acquires the location information of the set user (step S705).

[0073] The AI ​​unit 12 of the server 10 analyzes information that matches the user's preferences obtained in steps S407 and S503 from the basic information of the store or facility obtained in step S703, the user's location information obtained in S705, and the real-time information obtained in step S703 (step S706).

[0074] The output unit 13 of the server 10 notifies the target users of the real-time information narrowed down in step S706 (step S707).

[0075] [Program Overview] Next, an overview of a program according to one embodiment of the present invention will be described with reference to FIG.

[0076] 8 is a flowchart explaining the flow of a program according to an embodiment of the present invention. First, the user downloads an application program to the mobile terminal 20 (first user terminal) (step S801). However, the present invention is not limited to this, and in the case of, for example, SaaS (Software as a SERVICE), step S801 may be omitted. On the application program of the mobile terminal 20, the user registers a customer account (step S802), approves the provision of location information (step S803), registers ID linkage of SNS or MaaS (step S804), and registers initial preference information (step S805).

[0077] Next, the user displays a map screen (step S806) and selects a destination (step S807). When the user selects a destination, the map screen displays the current location, the destination, and detour information on the route from the current location to the destination. The user considers whether to make a detour (selecting multiple destinations), and if so, selects another destination (step S808). The detour information may be before or after the user's planned destination. For this reason, time information may be linked to the user's planned destination. By linking time information to the user's planned destination, the time of the detour information and the required time can be effectively combined, and more effective suggestions can be made to the user.

[0078] The user determines the route when all destinations have been decided (step S809). The program transitions to a navigation screen (step S810) and provides navigation according to the route determined in step S809. The navigation screen may be provided by an application program, or another navigation screen (e.g., Google Maps route guidance) may be used.

[0079] On the other hand, the store first downloads an application program to the mobile terminal 20 (second user terminal) (step S811). On the application program of the mobile terminal 20, the store registers a store account (step S812), approves the provision of location information (step S813), and registers ID linkage of the SNS (step S814).

[0080] The store may transmit information from an SNS site. The application program may extract the information transmitted on the SNS site and link it to location information. The information transmitted on the SNS site may be displayed as detour information on the map screen of the user's mobile terminal 20 together with basic store information. By linking the location information to the SNS as a suggestion from the store, notifications can be sent to customers who match their hobbies and preferences, and information can be suggested in real time. [Explanation of symbols]

[0081] 1... recommender system, 10... server, 11... input unit, 12... AI unit, 13... output unit, 20... mobile terminal, 21... providing unit, 22... input unit, 23... output unit, 30... network

Claims

1. A method for providing information by a program running on a server, the program comprising: an input unit; an AI unit for analyzing data; and an output unit; The input unit acquires user information including MaaS basic information necessary for ID federation of a MaaS and location information from a first user terminal, The input unit acquires user preference information including any one of transportation means selection information, behavior history, movement pattern, and transportation means utilization rate data from a MaaS account based on the MaaS basic information, and item information including any one of store, facility information, item information, and event information from an Internet site, The AI ​​unit uses the user preference information to analyze information that matches the user's preference among the item information using AI to generate an analysis result, The method includes: the output unit outputs map information including the analysis result and the location information to the first user terminal.

2. The method of claim 1 , further comprising prioritizing the analysis results to better match the user's preferences.

3. The method further includes acquiring surrounding information from the location information, The method of claim 1 or 2, wherein the item information includes the neighborhood information.

4. The method further includes acquiring store information from the second user terminal; The method of claim 1 , wherein the item information includes the store information.

5. The method of claim 4 , wherein the store information includes SNS basic information, and further comprising obtaining real-time information from an SNS account.

6. and notifying the second user terminal of the analysis result and the user information. The method according to claim 4 or 5, comprising:

7. and notifying the second user terminal when the analysis result is selected. The method according to any one of claims 4 to 6.

8. The method of claim 1 , wherein the item information includes event information.

9. The method of claim 1 , further comprising providing route guidance once the analysis result is selected.

10. A method according to any one of claims 1 to 9, The program for causing a computer to execute the program.

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