Recommendation system, program of recommendation system

Abstract

To provide a recommendation system in which a user can obtains both favorite destination information and route information as a set and perform a seamless movement.SOLUTION: A recommendation system 100 includes a user terminal 110 and a server 120. When information on a current situation of a user is input in the user terminal 110, in the user terminal, or the server, recommendation means having artificial intelligence narrows down destination information in consideration of movement means information on the basis of previously input taste information of the user, the input information on the current situation of the user, and current location information of the user terminal. Along with this, recommendation means narrows down route information, including the movement means information. Then, the recommendation means recommends by displaying the narrowed down route information and destination information as a set on a display section 111 of the user terminal 110.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a recommendation system that includes a user terminal and a server and recommends a destination to a user, and a program therefor.

Background Art

[0002] Conventionally, as a first aspect, a program for proposing an appropriate destination for a user is known (for example, Patent Document 1). Also, as a second aspect, a program for providing route information that matches the preferences and behavioral tendencies of a user's personal movement is known (for example, Patent Document 2).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, since the program of the above-described conventional first aspect only proposes a destination that matches the user's schedule without considering the user's preferences, the user has to separately search for a route and obtain route information, and there is a problem that the usability is not sufficient. Also, since the program of the above-described conventional second aspect only proposes route information, the user has to separately search for a destination and obtain destination information, and there is a problem that the usability is not sufficient.

[0005] Therefore, the present invention solves the problems of the prior art as described above. That is, the object of the present invention is to provide a recommendation system that enables a user to obtain both preferred destination information and route information in a set and perform seamless movement, and a program therefor.

Means for Solving the Problems

[0006] The invention according to claim 1 is a recommendation system that includes a user terminal and a server and recommends a destination to a user. When the current situation information of the user is input in the user terminal, in the user terminal or the server, the recommendation means having artificial intelligence narrows down the destination information in consideration of the means of movement based on the previously input user preference information, the input current situation information of the user, and the current location information of the user terminal, and also narrows down the route information including the means of movement, and displays the narrowed-down route information and destination information in a set on the display unit of the user terminal for recommendation. Moreover, at this time, the recommendation means has a plurality of logical methods of recommendation badges that prompt recommended actions. The logical methods of the plurality of recommendation badges are logically different in the way of prompting. The recommendation means is based on the user type tendency information obtained from the information input on the user terminal or the history information of the user terminal, and the type-specific logical tendency information obtained from the effectiveness of the logical methods of the past user types by type. Select one from among the logical methods of the plurality of recommendation badges, and display and recommend the destination information on the display unit of the user terminal in accordance with the selected logical method. By having such a configuration, the above-described problems are solved.

[0007] The invention according to claim 2, in addition to the configuration of the recommendation system described in claim 1, when the situation information includes weather and temperature information, and the current weather and temperature information is higher than the first predetermined temperature and hot, lower than the second predetermined temperature and cold, or the area including the current location and the destination is rainy, the recommendation means gives priority to movement on a route with a roof by bicycle or on foot, or movement by at least one of train, bus, car, and taxi over movement on a roofless route by bicycle and on foot, and recommends route information and destination information, thereby further solving the above-described problems.

[0008] The invention according to claim 3, in addition to the configuration of the recommendation system described in claim 1 or claim 2, wherein the situation information includes information on the degree of user fatigue, and when the current degree of fatigue information is higher than a predetermined degree, the recommendation means recommends route information and destination information that prioritize movement by at least one of a taxi and a train over movement by bicycle and walking, thereby further solving the above-described problems.

[0009] The invention according to claim 4, in addition to the configuration of the recommendation system described in any one of claims 1 to 3, wherein the situation information includes information on the calories consumed by the user and information on the degree of fatigue, and when the current degree of fatigue information is lower than a predetermined degree and the calorie intake information is higher than a predetermined value, the recommendation means recommends route information and destination information that prioritize movement by at least one of a bicycle and walking over movement by a taxi and a train, thereby further solving the above-described problems.

[0010] The invention according to claim 5, in addition to the configuration of the recommendation system described in any one of claims 1 to 4, wherein the situation information includes the user's baggage information, and when the current baggage information is more or heavier than a predetermined degree, the recommendation means recommends route information and destination information that prioritize movement by at least one of a bus, a train, a rental car, and a taxi over movement by bicycle and walking, thereby further solving the above-described problems.

[0011] The invention according to claim 6, in addition to the configuration of the recommendation system described in any one of claims 1 to 5, wherein the situation information includes the user's electronic payment information until immediately before the current day, and the recommendation means estimates at least one of the quantity and weight of the items the user has shopped for from the electronic payment information, and when it is determined that at least one of the estimated quantity and weight is more than a predetermined quantity or heavier than a predetermined weight, it recommends route information and destination information that prioritize movement by at least one of a bus, a train, a rental car, and a taxi over movement by bicycle and on foot, thereby further solving the above-described problems.

[0012] The invention according to claim 7, in addition to the configuration of the recommendation system described in any one of claims 1 to 6, wherein the situation information includes information on whether the user is currently using a car or a motorcycle, and in the case of using a car or a motorcycle, the recommendation means recommends destination information and route information for a destination with a parking lot or a destination with a parking lot around it and that does not mainly serve alcohol, thereby further solving the above-described problems.

[0013] The invention according to claim 8, in addition to the configuration of the recommendation system described in any one of claims 1 to 7, wherein the situation information includes the user's final destination information and information on whether the user is currently using a car or a motorcycle, and when there is a final destination and the user is using a car or a motorcycle, the recommendation means recommends route information including parking lot information for a transit point and the movement of a car or a motorcycle from the current location to the transit point, and the movement of a train or a bus from an intermediate destination to the final destination, as well as intermediate destination information, thereby further solving the above-described problems.

[0015] The invention according to claim 9 is a program for a recommendation system that includes a user terminal and a server and recommends a destination to a user. The user terminal or the server includes an input determination step of determining whether current situation information of the user has been input on the user terminal, and in the user terminal or the server, a recommendation means having artificial intelligence narrows down destination information in consideration of means of transportation information and narrows down route information including means of transportation information based on previously input user preference information, the input current situation information of the user, and the current location information of the user terminal. The recommendation means has a plurality of logical methods for recommendation nudges that logically prompt different forms of prompting the action of recommendation. Based on user type tendency information obtained from the information input on the user terminal or the history information of the user terminal and type-specific logical tendency information obtained from the effectiveness of type-specific logical methods of past user types, a recommendation nudge selection step of selecting one from among the plurality of logical methods of recommendation nudges, and a recommendation display step in which the user terminal displays and recommends the narrowed-down route information and destination information as a set on the display unit of the user terminal. At this time, the destination information is displayed and recommended on the display unit of the user terminal in accordance with the selected logical method. Cause to execute This solves the above-described problems.

Effects of the Invention

[0016] The recommendation system of the present invention includes a user terminal and a server, so that not only can communicate between the user terminal and the server to recommend a destination to the user, but also can achieve the following specific effects.

[0017] According to the recommendation system of the invention according to claim 1, based on the user's preference information and the current situation information, not only the destination information that the user wants to go to now, but also the destination information and route information that take into account the movable range within the free time according to the current traffic situation are recommended as a set. Therefore, the user can obtain both the destination information and the route information as a set and seamlessly move along them without thinking about "what should I do?", and furthermore, the travel time can be shortened and a new travel experience can be obtained. The "new travel experience" refers to the experience brought by seamless travel. In addition, it is possible to newly discover destinations in fields that one has not even considered through travel. Furthermore, since a recommendation badge suitable for the user is selected from among the logical methods of the plurality of recommendation badges and the recommendation along with it is displayed on the display unit of the user terminal, the user will try to select the recommended new destination regardless of the user's preference for known products, services, and places. That is, by approaching the user's potential preferences, the user's preferences are expanded, so that the user can be spontaneously selected to act on something (potential preferences) that the user did not want in the past. In addition, since the selection of something slightly different from the user's own hobbies and preferences is promoted, the bias in the user's behavior can be overcome and corrected.

[0018] According to the recommendation system of the invention according to claim 2, in addition to the effects achieved by the invention according to claim 1, when it is hot outdoors, cold, or raining, route information and destination information that prioritize moving along a route with a roof or moving by a vehicle with a roof are recommended. Therefore, the user can move to the destination comfortably.

[0019] According to the recommendation system of the invention according to claim 3, in addition to the effects achieved by the invention according to claim 1 or claim 2, when the user's fatigue level is relatively high, route information and destination information that prioritize moving by a vehicle that basically does not require the user to drive are recommended. Therefore, the user can move to the destination comfortably without performing a driving operation.

[0020] According to the recommendation system of the invention according to claim 4, in addition to the effects achieved by the invention according to any one of claims 1 to 3, when the user's fatigue level is relatively low and the user has consumed a relatively large amount of calories through nutrition supplementation such as meals, basically, route information and destination information that prioritize movement by the user's own exercise are recommended. Therefore, the user can move to the destination while actively consuming calories.

[0021] According to the recommendation system of the invention according to claim 5, in addition to the effects achieved by the invention according to any one of claims 1 to 4, when the user has relatively many belongings or heavy belongings, route information and destination information that prioritize movement using vehicles such as buses, trains, rental cars, and taxis, which allow the user to carry them with little effort, are recommended. Therefore, the user can comfortably move to the destination with little effort in carrying the belongings.

[0022] According to the recommendation system of the invention according to claim 6, in addition to the effects achieved by the invention according to any one of claims 1 to 5, similar to the effect of the invention of claim 5, when the user has relatively many belongings or heavy belongings, route information and destination information that prioritize movement using vehicles such as buses, trains, rental cars, and taxis, which allow the user to carry them with little effort, are recommended. Therefore, the user can comfortably move to the destination with little effort in carrying the belongings.

[0023] According to the recommendation system of the invention according to claim 7, in addition to the effects achieved by the invention according to any one of claims 1 to 6, when using a car or a motorcycle, destination information where a parking lot can be used is recommended. Therefore, the user can park at the destination without being troubled by the place to park the car or the motorcycle. Furthermore, since the destination does not mainly provide alcohol, the temptation to drink can be avoided, and the risk of drunk driving afterwards can be avoided.

[0024] According to the recommendation system of the invention according to claim 8, in addition to the effects achieved by the invention according to any one of claims 1 to 7, since there is a final destination and, in the case of using a car or a motorcycle, destination information on the way via a parking lot is recommended, the user can do the so-called park-and-ride, changing from traveling by car or motorcycle to traveling by train or bus, and enjoy a recommended experience at the intermediate destination.

[0026] This claim 9 According to the program of the recommendation system of the invention according to this claim, similar to the effects achieved by the invention according to claim 1, not only destination information that the user currently wants to go to based on the user's preference information and current situation information, but also destination information and route information considering the movable range within the free time according to the current traffic situation are recommended as a set. Therefore, the user can obtain both destination information and route information as a set and perform seamless movement along them without thinking about "what should I do?", and furthermore, can shorten the travel time and obtain a new travel experience. Also, it is possible to newly discover a destination in a field that one has not even considered by moving. Furthermore, since a recommendation badge suitable for the user is selected from among the logical methods of the plurality of recommendation badges and the recommendation along with it is displayed on the display unit of the user terminal, the user will try to select the recommended new destination regardless of the user's preference for known products, services, and places. That is, by approaching the user's potential preferences, the user's preferences are expanded, so that the user can be spontaneously selected to act on something (potential preferences) that the user did not want in the past. In addition, since the selection of something slightly different from the user's own hobbies and preferences is promoted, the bias in the user's behavior can be overcome and corrected.

Brief Description of the Drawings

[0027]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Mode for Carrying Out the Invention

[0028] The recommendation system of the present invention includes a user terminal and a server. When current situation information of a user is input in the user terminal, in the user terminal or the server, recommendation means having artificial intelligence narrows down destination information in consideration of means of transportation information based on pre-input user preference information, the input current situation information of the user, and the current location information of the user terminal, and also narrows down route information including means of transportation information. By being configured to display, in a set, the narrowed-down route information and destination information on a display unit of the user terminal for recommendation, the user can obtain both the destination information and the route information in a set and can perform seamless movement along them without thinking about "what should I do?", and furthermore, as long as it can shorten the travel time and obtain a new travel experience, its specific implementation mode can be any. In addition, the program of the recommendation system of the present invention includes an input determination step in which a user terminal or a server determines whether current situation information of the user has been input in the user terminal, and in the user terminal or the server, a recommendation means having artificial intelligence narrows down destination information in consideration of means of transportation information based on previously input user preference information, the input current situation information of the user, and the current location information of the user terminal, and also narrows down route information including the means of transportation information. The user terminal has a recommendation display step of displaying the narrowed-down route information and destination information as a set on the display unit of the user terminal for recommendation. By doing so, the user can obtain both the destination information and the route information as a set, and can seamlessly move along it without thinking about "what should I do?", and further, if it is possible to shorten the travel time and obtain a new travel experience, the specific implementation mode can be any.

[0029] For example, the user terminal may be a notebook personal computer terminal, a smartphone terminal, a tablet terminal, a wristwatch-type terminal, a glasses-type terminal, etc., which has a display unit and an operation unit and transmits and receives information, and can be connected to a server through a communication network including a wide area network such as the so-called Internet, a local network, a telephone line, etc., and can be any device as long as it can be connected to the server. Also, the server may be a cloud server created in a cloud environment, and the number of physical servers constituting the server may be one or more. "New travel experience" refers to the experience of seamless movement.

Example

[0030] Hereinafter, the recommendation system 100 which is an embodiment of the present invention will be described with reference to FIGS. 1 to 8(C). Here, FIG. 1 is a diagram showing the concept of the recommendation system 100 which is an embodiment of the present invention, FIG. 2 is a chart diagram showing an operation example of the recommendation system 100 which is an embodiment of the present invention, FIG. 3 is a diagram showing the my page screen 112 of the recommendation system 100 which is an embodiment of the present invention, FIG. 4 is a diagram showing your mood screen 113 which freely displays the current situation information ST of the recommendation system 100 which is an embodiment of the present invention, FIG. 5 is a diagram showing the recommendation screen 114 of the recommendation system 100 which is an embodiment of the present invention, FIG. 6(A) is a diagram showing an example of a method of classifying user types of the recommendation system 100 which is an embodiment of the present invention, FIG. 6(B) is a diagram showing an example of estimating the user type tendency information TP of a user of the recommendation system 100 which is an embodiment of the present invention, FIGS. 7(A) to 7(C) are diagrams showing an example of the logical method RN of the recommendation badge of the recommendation system 100 which is an embodiment of the present invention, FIGS. 8(A) to 8(C) are diagrams showing an example of the visual expression method UI of the recommendation user interface of the recommendation system 100 which is an embodiment of the present invention, and FIG. 9 is a diagram showing a display example of the display unit 111 of the user terminal 110 of the recommendation system 100 which is an embodiment of the present invention.

[0031] As shown in FIG. 1, the recommendation system 100 which is an embodiment of the present invention includes a user terminal 110, a server 120, and a store terminal 130 which is a commodity / service / location provider terminal as an example. The user terminal 110 and the store terminal 130 are provided so as to be able to communicate with the server 120. And the recommendation system 100 has a database 121 of information such as the latest commodities, services, and locations input from the store terminal 130 or the like, and is provided to recommend destinations to users.

[0032] The store terminal 130 uploads, for example, on a social networking service (hereinafter referred to as SNS) or updates the information of the store's website by sending information about the store's commodities, services, and locations to the server 120. Also, the user logs in to the recommendation system 100 on the user terminal 110. And the user's preference information LK is input in advance.

[0033] For example, on the "Things You Like" screen (not shown) of the recommendation system 100 displayed on the display unit 111 of the user terminal 110, there are categories such as gourmet, cafe, outdoor, shopping, going out, entertainment, fashion, and indoor. A plurality of photos related to the category are displayed within each category. Then, the user selects one or more photos in which their favorite things are shown. Tag information indicating the content shown in each photo is added. When a photo is selected by the user, the tag information of the selected photo is configured to be added as the preference information LK of the user's favorite things. In addition to photos, it may also be configured to select icons, keywords, voices, etc. Something is represented by the icons, keywords, and voices. In the case of voices, a button is displayed, and when the button is operated, something is output as voice. When the button is selected, the tag information of the content of the button is added as the preference information LK. Also, the user may use the microphone on the user terminal 110 to input voice about their favorite things, or use the input key to input text about their favorite things in sentences or words, and configure the information to be added as the preference information LK.

[0034] Furthermore, on the "Your Personality" screen (not shown) of the recommendation system 100 displayed on the display unit 111 of the user terminal 110, a plurality of simple questions are provided. For example, questions such as "I like to meet people for the first time and can enjoy conversations with them even on the first meeting" are provided. Then, as an example, like a five - level evaluation, it is provided so that the user can intuitively answer each question with options such as "not applicable at all", "barely applicable", "neither applicable nor not applicable", "somewhat applicable", "completely applicable". When these answers are input by the user, in the user terminal 110 or the server 120, the recommendation means with artificial intelligence analyzes the answer information to obtain the user type tendency information TP.

[0035] After that, when the current situation information ST of the user is input in the user terminal 110, in the user terminal 110 or the server 120, the recommendation means with artificial intelligence narrows down the destination information DN in consideration of the means of transportation information based on the previously input user preference information LK, the input current situation information ST of the user, and the current location information of the user terminal 110. At the same time, the recommendation means narrows down the route information RT including the means of transportation information. Here, narrowing down means reducing a large number of information to a small number of information, and the small number can be one or more. The destination information DN and the route information RT before narrowing down are not limited to those in the database 121, and information in other systems or information in social networking services may also be used. And the recommendation means is configured to display and recommend the narrowed - down route information RT and the destination information DN as a set on the display unit 111 of the user terminal 110.

[0036] Thereby, not only the destination information DN that the user wants to go to now based on the user preference information LK and the current situation information ST, but also the destination information DN and the route information RT considering the movable range within the free time according to the current traffic situation are recommended as a set. As a result, without the user thinking about "what should I do?", the user can perform seamless movement along it, and furthermore, the movement time can be shortened and a new movement experience can be obtained. That is, it is possible to personalize the destination and the means of transportation according to the user's preferences and situation and make an optimal proposal.

[0037] Subsequently, an operation example of the recommendation system 100 of the present invention will be described in more detail. As shown in FIG. 2, in step S1, as a preference information input determination step, the user terminal 110 or the server 120 determines whether or not the user has input the user's preference information LK on the user terminal 110. For example, in the case of a selection operation of a photo on the above-described "things I like" screen, it is determined that there is an input. If it is determined that there is an input, the process proceeds to step S2. On the other hand, if it is determined that there is still no input, step S1 is repeated.

[0038] In step S2, as a preference information registration step, the user terminal 110 or the server 120 registers the user's preference information LK in the database 121 in the server 120. As shown in FIG. 3, when the user's preference information LK is registered, the user's preference information LK is displayed on the my page screen 112 of the recommendation system 100 shown on the display unit 111 of the user terminal 110. More specifically, the tag information of the photo selected by the user is registered as the preference information LK, and these tag information are configured to be displayed in the item of "things I like".

[0039] Note that the recommendation means may be configured to display the user type tendency information TP obtained based on the response information answered on the above-described "your personality" screen (not shown) in the item of "your personality" on the my page screen 112. As an example, the user type tendency information TP has items such as openness, honesty, extroversion, cooperation, and neurotic tendency, and is configured to be displayed by level values in each item. These user type tendency information TPs are configured to be registered in the database 121 in the server 120, similar to the user preference information LK.

[0040] In step S3, as a situation information input determination step, the user terminal 110 or the server 120 determines whether the current situation information ST of the user has been input in the user terminal 110. For example, as shown in FIG. 4, on the your mood screen 113 of the recommendation system 100 shown on the display unit 111 of the user terminal 110, the current situation information ST of the user is provided so that it can be input. On the your mood screen 113, input items are provided for information about the people together, free time information, appetite and material desire information, and active and rest desire information. Regarding the item of information about the people together, as an example, options such as alone, couple, friends, family, etc. are set so that it can be known whether the user is alone or with whom. Furthermore, regarding the item of free time information, it is provided so that the current amount of free time can be freely input.

[0041] Also, regarding the item of appetite and material desire information, it is provided so that it can be freely input which one has a greater proportion. Furthermore, regarding the item of active and rest desire information, it is provided so that it can be freely input which one has a greater proportion. Regarding these, when there is an input, the user terminal 110 or the server 120 determines that the current situation information ST of the user has been input. If it is determined that there is an input, proceed to step S4. On the other hand, if it is determined that there is still no input, repeat step S3.

[0042] Note that as the current situation information ST of the user, items for inputting information about the current use of a car or a motorcycle, the user's luggage information, the user's fatigue level information, the number of steps walked, and the calorie intake information may be provided. Furthermore, as the current situation information ST of the user, not only what the user inputs, but also location information, date and time information, weather and temperature information, electronic payment information such as IC cards and electronic money, etc. may be automatically input. Basically, it moves from the current location to the destination within free time, has some experience at the destination, and then moves back from the destination to the current location. However, in order to handle the case where it does not return to the current location, an item for inputting the user's final destination information may be provided. For example, it is used when the final destination is determined and the user wants to go to the final destination via some destination during free time. Regarding the input method, the camera of the user terminal 110 may be used to photograph the user's facial expression, and the fatigue degree information may be estimated and input using image analysis. Also, the movement step information may be input using the walking history information of the pedometer function of the user terminal 110, and the fatigue degree information may be estimated. Furthermore, the movement distance may be estimated based on the position information of the user terminal 110, the calorie consumption information may be estimated, and the fatigue degree information may be estimated. Also, the camera of the user terminal 110 may be used to photograph the intake such as food, and the intake calorie information may be estimated using image analysis. Furthermore, it may be configured such that the body temperature information, heart rate information, movement distance information, movement step information, etc. of the user are input using a smart watch terminal which is a wristwatch-type terminal worn on the user's wrist. Also, the camera may be used to photograph the user's luggage, and the luggage information may be estimated and input using image analysis. Furthermore, the camera may be used to photograph a car or a motorcycle, and the information on the current presence or absence of car use and motorcycle use may be estimated and input using image analysis. Also, the input is not limited to being by the user's operation. For example, it may be configured to be automatically input at all times, or it may be configured to be automatically input when the application of the recommendation system 100 is started.

[0043] In step S4, as a narrowing-down step, in the user terminal 110 or the server 120, the recommendation means having artificial intelligence narrows down the destination information DN in consideration of the means-of-movement information based on the previously input user preference information LK, the input current situation information ST of the user, and the current location information of the user terminal 110. At the same time, the recommendation means narrows down the route information RT including the means-of-movement information. Note that the destination information DN may be registered in the database 121 of the server 120 or may be in a social networking service (SNS), and is narrowed down from among them. That is, it may be possible to cooperate with other systems to narrow down and pick up the destination information DN registered in other systems. Also, the destination may be narrowed down in consideration of the means-of-movement information of other systems. "Consideration" means considering the conditions of the available means-of-movement information obtained from the preference information LK, the situation information ST, and the current location information, and the route information when using such means of movement. For example, consider train, subway, and bus timetable information, transfer time information, delay information, road traffic volume and congestion information, congestion prediction information, available car rental information near the location, taxi dispatch information in the vicinity, parking lot information at the destination and its vicinity, etc. As a more specific example, consider that the next train stops at each station, but the following train is an express train, and using the express train allows one to go further and return within the available time range compared to using each station stop. Also, since a bus is just arriving at a bus stop near the current location, consider the case where the recommended destination farthest from the current location among the narrowed-down destination information DN is the most accessible and optimal. Note that when narrowing down the destination information DN in consideration of the means-of-movement information, campaign information such as free, discounted, and coupon information for the means of movement and the destination, which is an example of information beneficial to the user, may be picked up and incorporated.

[0044] In step S5, as a recommendation display step, the user terminal 110 sets the filtered route information RT and the destination information DN and displays them on the display unit 111 of the user terminal 110 as a set to make a recommendation. As shown in FIG. 5, as an example, the display unit 111 of the user terminal 110 displays a recommendation screen 114. On the recommendation screen 114, map information including the current location of the user terminal 110, icons IC of one or more filtered destination information DN, and route information RT from the current location to one or more destinations are displayed.

[0045] As a result, not only the destination information DN that the user wants to go to now based on the user's preference information LK and the current situation information ST, but also the destination information DN and the route information RT that take into account the movable range within the free time according to the current traffic situation are recommended as a set. As a result, without the user thinking about "what should I do?", the user can see the recommendations presented by the recommendation means. If the user likes them, the user can move seamlessly along them, further shortening the travel time and obtaining a new travel experience. Regarding the route information RT, when the user taps the route or performs other operations, detailed route information RT including transportation means information and transfer information is provided to be displayed. Also, regarding the destination information DN, when the user taps the icon IC or performs other operations, detailed destination information DN including the business hours, specialties, recommended items, parking lot information, homepage information, etc. of the destination is provided to be displayed. Note that as a candidate selection, the destination information DN and the route information RT with the shortest time may be configured to be displayed.

[0046] Furthermore, in this embodiment, the situation information ST includes weather and temperature information. When the current weather and temperature information is hotter than the first predetermined temperature, colder than the second predetermined temperature, or the area including the current location and the destination is rainy, the recommendation means is configured not to actively recommend the route information RT for moving by bicycle and on foot without a roof. Instead, it is configured to recommend route information RT that prioritizes moving by bicycle or on foot with a roof, moving by at least one of train, bus, car, and taxi, and destination information DN. Thereby, when it is hot outdoors, cold, or raining, route information RT and destination information DN that prioritize moving by a route with a roof or by a vehicle with a roof are basically recommended. As a result, the user can move to the destination comfortably.

[0047] Also, in this embodiment, the situation information ST includes information on the degree of fatigue of the user. When the current degree of fatigue information is higher than a predetermined degree, the recommendation means is configured not to actively recommend the route information RT for moving by bicycle and on foot. Instead, it is configured to recommend route information RT that prioritizes moving by at least one of taxi and train and destination information DN. Thereby, when the user's degree of fatigue is relatively high, route information RT and destination information DN that prioritize moving by a vehicle that does not require the user to perform a driving operation are basically recommended. As a result, the user can move to the destination comfortably without performing a driving operation.

[0048] Also, the situation information ST includes calorie intake information in addition to the user's degree of fatigue information. When the current degree of fatigue information is lower than a predetermined degree and the calorie intake information is higher than a predetermined value, the recommendation means is configured not to actively recommend the route information RT for moving by taxi and train. Instead, rather than this, the recommendation means is configured to recommend route information RT and destination information DN that prioritize movement by at least one of a bicycle and walking. Accordingly, when the user's fatigue level is relatively low and the user has consumed a relatively large amount of calories through nutrition supplementation such as meals, route information RT and destination information DN that prioritize movement by the user's own exercise are basically recommended. As a result, the user can move to the destination while actively consuming calories.

[0049] Furthermore, in this embodiment, the situation information ST includes the user's baggage information. When the current baggage information is more or heavier than a predetermined degree, the recommendation means is configured not to actively recommend route information RT for movement by bicycle and walking. Instead, rather than this, the recommendation means is configured to recommend route information RT and destination information DN that prioritize movement by at least one of a bus, a train, a rental car, and a taxi. Accordingly, when the user has relatively a lot of baggage, when it is heavy, route information RT and destination information DN that prioritize movement using a bus, a train, a rental car, or a taxi, which are vehicles that the user can carry with little effort, are recommended. As a result, the user can comfortably move to the destination with little effort in carrying the baggage.

[0050] Also, in this embodiment, the situation information ST includes the user's electronic payment information until immediately before the current day. The recommendation means estimates at least one of the quantity and weight of the items the user has purchased from the electronic payment information. And when it is determined that at least one of the estimated quantity and weight is more than a predetermined quantity or heavier than a predetermined weight, the recommendation means is configured not to actively recommend route information RT for movement by bicycle and walking. Instead, rather than this, the recommendation means is configured to recommend route information RT and destination information DN that prioritize travel by at least one of bus, train, rental car, and taxi. Thereby, when the user has relatively a lot of luggage, in case of heavy luggage, route information RT and destination information DN that prioritize travel using a bus, train, rental car, or taxi, which are vehicles that the user can use with little carrying on their own, are recommended. As a result, the user can comfortably travel to the destination with little effort in carrying the luggage.

[0051] Furthermore, in this embodiment, the situation information ST includes information on whether the user is currently using a car or a motorcycle. In the case of using a car or a motorcycle, the recommendation means is configured to recommend destination information DN and route information RT for a destination with a parking lot or a destination with a parking lot around it and that does not mainly serve alcohol. Thereby, when using a car or a motorcycle, destination information DN where a parking lot can be used is recommended. As a result, the user can park at the destination without being troubled by a place to park the car or motorcycle. Furthermore, the destination does not mainly serve alcohol. As a result, the temptation to drink alcohol can be avoided and the risk of drunk driving afterwards can be avoided.

[0052] Also, in this embodiment, the situation information ST includes the user's final destination information, information on current car use or motorcycle use. When there is a final destination and the user is using a car or a motorcycle, the recommendation means is configured to recommend route information RT including parking lot information for a transit point and travel by car or motorcycle from the current location to the transit point, and travel by train or bus from an intermediate destination to the final destination, as well as intermediate destination information DN. As a result, when there is a final destination and in the case of using a car or a motorcycle, the destination information DN on the way via a parking lot is recommended. As a result, the user can change from traveling by car or motorcycle to traveling by train or bus, that is, do a so-called park-and-ride, and enjoy a recommended experience at the intermediate destination.

[0053] In addition, as the current situation information ST of the user, an item for inputting information about the current clothing may be provided. For example, the user's clothing can be photographed using the camera of the user terminal 110, and information on whether the clothing is active or not can be estimated and input using image analysis. If it is active clothing, the recommendation means can recommend destination information DN where an active experience can be had to the user. On the other hand, if it is not active clothing, the recommendation means does not recommend destination information DN where an active experience can be had to the user, but recommends destination information DN where one can relax calmly or leisurely to the user. The same applies to the means of transportation information. For example, when the clothing is not active such as high heels and a skirt, the recommendation means does not recommend route information RT using means of transportation such as a bicycle (including rental) or a motorcycle (including rental), but recommends route information RT using means of transportation such as a taxi or a rental car. In addition, as the current situation information ST of the user, an item for inputting information on whether the user is currently using a bicycle may be provided. For example, a bicycle can be photographed using a camera, and information on whether a bicycle is being used or not can be estimated and input using image analysis. If a bicycle is being used, the recommendation means recommends destination information DN and route information RT that are easily accessible by bicycle. For example, destination information DN where there is a bicycle parking lot at or around the destination is recommended. In addition, route information RT of a route with few slopes or a route that is easy for bicycles to pass through is recommended. As a route that is easy for bicycles to pass through, for example, there is not only a route with a wide traffic lane, but also a route that is less affected by strong winds in cooperation with weather information. In addition, as the current situation information ST of the user, information on the presence or absence of interest in events (limited period, seasonal flower information) and information on the presence or absence of interest in sales may be included. Also, the recommended destination information DN may be recommended to go to a plurality of destinations in order at once. For example, it may be recommended that "if you go to this destination, it is also a good idea to go here together!"

[0054] Furthermore, in this embodiment, the recommendation means has a plurality of logical methods RN of recommendation nudges that prompt recommended actions. For example, a plurality of logical methods RN of recommendation nudges are set in the database 121 of the server 120. The logical methods RN of the plurality of recommendation nudges are logically different in the way they prompt. And the recommendation means obtains user type tendency information TP from the information input on the user terminal 110 or the history information of the user terminal 110. Furthermore, the recommendation means selects one from among the plurality of logical methods RN of recommendation nudges based on the obtained user type tendency information TP and the type-specific logical tendency information obtained from the effectiveness of the type-specific logical methods RN of past user types.

[0055] More specifically, the type-specific logical tendency information is an evaluation of the effectiveness of the type-specific logical methods RN of each user type in the past, and has information on which logical method RN of which recommendation nudge is most effective for which user type tendency information TP. "Effective" means that in the recommendation system 100, for products, services, and places that the user has spontaneously recommended in the past, when it is determined that the user has favorited them or visited the places where those products, services, and places are provided, it is evaluated that the logical method RN of the recommendation nudge is effective for that user. Here, "most effective" may be either the one with the highest evaluation of effectiveness during a predetermined period or the one with the highest increasing trend in the evaluation of effectiveness in the most recent short period. Then, the recommendation means selects the most effective logical method RN of the recommendation nudge for the user type tendency information TP about the user of the user terminal 110. Furthermore, the recommendation means is configured to display the destination information DN on the display unit 111 of the user terminal 110 along the selected logical method RN and make a recommendation.

[0056] As a result, a recommendation nudge suitable for the user is selected from among a plurality of logical methods RN of the recommendation nudge, and a recommendation along with it is displayed on the display unit 111 of the user terminal 110. As a result, regardless of the user's preferences for products, services, and places they know, the user will try to select the recommended new destination. That is, it approaches the user's potential preferences and expands the user's preferences. As a result, it is possible to cause the user to spontaneously select and act on something (potential preferences) that the user did not desire in the past. Furthermore, it encourages the user to make selections that are slightly away from their own hobbies and preferences. As a result, it is possible to overcome and correct the bias in the user's behavior.

[0057] More specifically, as described above, in the user type tendency information acquisition step, the user terminal 110 or the server 120 obtains the user type tendency information TP from the information input in the above-mentioned "Your Personality" screen (not shown) displayed on the user terminal 110 or the history information of the user terminal 110. In other words, it determines the user type regarding the user's personality aspect. As shown in FIG. 6(A), as an example of the determination of the user type, the user terminal 110 or the server 120 has a user type classification means with artificial intelligence.

[0058] And the user type classification means obtains the class classification information of the user type from the big data. As a specific example, the big data includes facility information, store information, event information, and information about the user. And the user type classification means obtains, from the big data, as an example of the personality type classification, class classification information with elements such as openness, honesty, extroversion, cooperativeness, and neurotic tendency.

[0059] Furthermore, the user type classification means obtains at least one of the user's personal information, preference information LK, action history information, and SNS information, and estimates the user type tendency information TP based on the class classification information from the obtained information. The user's personal information and preference information LK include the information of the answer content and the answer time when answering several questions on the "Your Personality" screen for the user's personality analysis after login. As a specific example, as shown in FIG. 6(B), the user type classification means obtains at least one of the user's personal information, preference information LK, action history information, and SNS information, and evaluates the user's personality aspect respectively with elements of openness, honesty, extroversion, cooperativeness, and neurotic tendency from the obtained information. Then, for example, it is estimated that the user type tendency information TP regarding the user's personality aspect is highly cooperative, followed by a neurotic tendency and a tendency towards high openness, while on the other hand, there is a tendency towards low extroversion, and it is also an efficiency-oriented type, etc.

[0060] As a result, the classification of the user's type becomes a classification along with the situation in the world. As a result, it is possible to obtain user classification information along with the situation in the world. Furthermore, the current information regarding the user's type is reflected in the user type tendency information TP. As a result, the user type tendency information TP can be accurately estimated. Also, it becomes possible to respond to changes in the user's classification and type. As a result, the current user type tendency information TP can be accurately estimated.

[0061] Next, as the recommendation nudge selection step, the user terminal 110 or the server 120 selects one from among a plurality of logical methods RN of the recommendation nudge based on the obtained user type tendency information TP and the type-specific logical tendency information obtained from the effectiveness of the type-specific logical method RN of the past user type. Here, as shown in FIGS. 7(A) to 7(C), the user terminal 110 or the server 120 has a plurality of logical methods RN of the recommendation nudge that promote recommended actions in different logical promotion forms.

[0062] As shown in FIG. 7(A), as an example of the recommendation nudge, for example, there is a logical idea of prompting the user by displaying the state where the remaining quantity decreases in a short time because "many people are buying". As a specific example, regarding the croissant in the bakery, for example, it is displayed as "At 12:15, there are still '82' left!" and the remaining quantity is counted down.

[0063] Also, as shown in FIG. 7(B), as an example 2 of the recommendation nudge, for example, there is a logical way of thinking to prompt the user to take action by displaying three price ranges and displaying the one that is most desired to be promoted in the middle price range. As a specific example, for the croissant in a bakery, for example, present three price ranges as "Option A Croissant 120 yen, Option B Croissant with ○○ Butter 400 yen, Option C Bread Set 800 yen" and display the one that is most desired to be promoted in the middle price range.

[0064] Furthermore, as shown in FIG. 7(C), as an example 3 of the recommendation nudge, for example, there is a logical way of thinking to prompt the user to take action by displaying a limited period and location. As a specific example, for the croissant in a bakery, for example, display it with a limited period and location as "Limited sale of freshly baked croissants at the ○○ Park market from May 1st to May 5th!"

[0065] For example, assume that there is type-specific logical tendency information such that, as user type tendency information TP, when the cooperativeness among the evaluation elements of the user's personality aspect is high, based on past performance, the example 1 of the recommendation nudge shown in FIG. 7(A) is the most effective. And, as the user's user type tendency information TP, when the cooperativeness among the evaluation elements of the user's personality aspect is high, the user terminal 110 or the server 120 selects the example 1 of the recommendation nudge shown in FIG. 7(A).

[0066] Also, for example, assume that there is type-specific logical tendency information such that, as user type tendency information TP, when the neurotic tendency among the evaluation elements of the user's personality aspect is high, based on past performance, the example 2 of the recommendation nudge shown in FIG. 7(B) is the most effective. And, as the user's user type tendency information TP, when the neurotic tendency among the evaluation elements of the user's personality aspect is high, the user terminal 110 or the server 120 selects the example 2 of the recommendation nudge shown in FIG. 7(B).

[0067] Furthermore, for example, as the user type tendency information TP, if the extroversion and openness among the evaluation elements of the user's personality aspect are high, based on past achievements, assume that there is type-specific logical tendency information indicating that Example 3 of the recommendation nudge shown in FIG. 7(C) is the most effective. And as the user type tendency information TP of the user, if the extroversion and openness among the evaluation elements of the user's personality aspect are high, the user terminal 110 or the server 120 selects Example 3 of the recommendation nudge shown in FIG. 7(C). Note that, for ease of understanding, the explanation has been given focusing on one evaluation element, but of course, it may be configured to select based on an overall view of the five elements according to the chart shape in FIG. 6(B).

[0068] Subsequently, as the recommendation user interface selection step, the user terminal 110 or the server 120 selects one or more from among the visual expression methods UI of a plurality of recommendation user interfaces based on the obtained user type tendency information TP and the type-specific visual expression tendency information obtained from the effectiveness of the type-specific visual expression methods UI of the past user types. Here, the type-specific visual expression tendency information is obtained by evaluating the effectiveness of the type-specific visual expression methods UI of each user type in the past, and has information on which visual expression method UI of which recommendation user interface is the most effective for which user type tendency information TP. "Effective" means that in the recommendation system 100, for the products, services, and places that the user was spontaneously recommended in the past, it is determined that the user favorited them or went to the places where those products, services, and places are provided, so that the visual expression method UI of that recommendation user interface is evaluated to be effective for that user. Here, "the most effective" may be either the one with the highest effectiveness evaluation in a predetermined period or the one with the highest increasing tendency in the effectiveness evaluation in the most recent short period. Furthermore, the user terminal 110 or the server 120 combines the selected visual representation method UI with the selected logical method RN.

[0069] Here, as shown in FIGS. 8(A) to 8(C), the user terminal 110 or the server 120 has a plurality of visual representation methods UI of the recommended user interface that is a visual representation. The visual representation methods UI of the plurality of recommended user interfaces have different visual priorities respectively. For example, the visual representation methods UI of the plurality of recommended user interfaces are set in the database 121 of the server 120.

[0070] As shown in FIG. 8(A), as an example 1 of the recommended user interface, for example, there is a way of visual expression that prompts the user to take action by visually emphasizing time. As a specific example, for the croissant in the bakery, for example, it is displayed emphasizing the time, such as "Remaining '1 hour 50 minutes'!".

[0071] Also, as shown in FIG. 8(B), as an example 2 of the recommended user interface, there is a way of visual expression that prompts the user to take action by displaying using a visually photogenic image. As a specific example, for the croissant in the bakery, for example, a photogenic image of a mobile bakery is used and this is displayed as the main.

[0072] Furthermore, as shown in FIG. 8(C), as an example 3 of the recommended user interface, there is a way of visual expression that prompts the user to take action by displaying the calorie display more prominently than the price display visually. As a specific example, for the croissant in the bakery, for example, '447.9 kcal!' is displayed more prominently than '180 yen'.

[0073] As another example of the recommendation user interface, there is a way to prompt the user to take action with a visual expression by displaying it in a short and comfortable sentence. Also, as another example of the recommendation user interface, there is a way to prompt the user to take action with a visual expression by displaying a message such as "Only ○ left" to encourage early reservation. Furthermore, as another example of the recommendation user interface, there is a way to prompt the user to take action with a visual expression by displaying a message such as "One more and the hood loss will be eliminated". In addition, as another example of the recommendation user interface, there is also a way to add an audio expression to the visual expression to prompt the user to take action by adding not only visual but also audio to the display.

[0074] For example, assuming that the user type tendency information TP is estimated to be an efficiency-oriented type from the evaluation of the user's personality aspect, and based on past performance, there is type-specific visual expression tendency information indicating that the example 1 of the recommendation user interface shown in FIG. 8(A) is the most effective. And when the user type tendency information TP of the user is estimated to be an efficiency-oriented type from the evaluation of the user's personality aspect, the user terminal 110 or the server 120 selects the example 1 of the recommendation user interface shown in FIG. 8(A).

[0075] Also, for example, assuming that the user type tendency information TP is estimated to be a visually-oriented type from the evaluation of the user's personality aspect, and based on past performance, there is type-specific visual expression tendency information indicating that the example 2 of the recommendation user interface shown in FIG. 8(B) is the most effective. And when the user type tendency information TP of the user is estimated to be a visually-oriented type from the evaluation of the user's personality aspect, the user terminal 110 or the server 120 selects the example 2 of the recommendation user interface shown in FIG. 8(B).

[0076] Furthermore, for example, when the user type tendency information TP is estimated as a health-oriented type from the evaluation of the user's personality aspect, based on past performance, assume that there is type-specific visual expression tendency information indicating that Example 3 of the recommended user interface shown in FIG. 8(C) is the most effective. And when the user type tendency information TP of the user is estimated as a health-oriented type from the evaluation of the user's personality aspect, the user terminal 110 or the server 120 selects Example 3 of the recommended user interface shown in FIG. 8(C).

[0077] Also, for example, when the user type tendency information TP is estimated as a liberal arts type from the evaluation of the user's personality aspect, based on past performance, assume that there is type-specific visual expression tendency information indicating that Example 4 of the recommended user interface is the most effective. And when the user type tendency information TP of the user is estimated as a liberal arts type from the evaluation of the user's personality aspect, the user terminal 110 or the server 120 selects Example 4 of the recommended user interface.

[0078] Furthermore, for example, when the user type tendency information TP is estimated as a worrying type from the evaluation of the user's personality aspect, based on past performance, assume that there is type-specific visual expression tendency information indicating that Example 5 of the recommended user interface is the most effective. And when the user type tendency information TP of the user is estimated as a worrying type from the evaluation of the user's personality aspect, the user terminal 110 or the server 120 selects Example 5 of the recommended user interface.

[0079] Also, for example, when the user type tendency information TP is estimated as a contributing type from the evaluation of the user's personality aspect, based on past performance, assume that there is type-specific visual expression tendency information indicating that Example 6 of the recommended user interface is the most effective. When, as the user type tendency information TP of the user, it is estimated as the contribution type from the evaluation of the user's personality aspect, the user terminal 110 or the server 120 selects Example 6 of the recommended user interface.

[0080] Furthermore, for example, when it is estimated as the sound type from the evaluation of the user's personality aspect as the user type tendency information TP, assuming there is type-specific visual expression tendency information indicating that Example 7 of the recommended user interface is the most effective based on past performance. When, as the user type tendency information TP of the user, it is estimated as the sound type from the evaluation of the user's personality aspect, the user terminal 110 or the server 120 selects Example 7 of the recommended user interface.

[0081] Note that the evaluation of the user's personality aspect as the user type tendency information TP in the recommended user interface selection step may be the same as the evaluation of the user's personality aspect as the user type tendency information TP in the recommended nudge selection step, or the evaluation may be made by focusing on different elements respectively. Furthermore, regarding the selection of the recommended user interface in the recommended user interface selection step, it is not limited to one, and multiple selections may be combined.

[0082] Subsequently, as an example, in the category selection operation determination step, the user terminal 110 or the server 120 determines whether the user has performed an operation of selecting the destination category on the user terminal 110. If it is determined that the operation has been performed, the process proceeds to the next recommended display step S5. On the other hand, if it is determined that the operation has not been performed yet, the category selection operation determination step is repeated. Note that the category selection operation determination step is not essential.

[0083] Subsequently, in the above-described recommendation display step S5, the user terminal 110 or the server 120 sets and recommends to the display unit 111 of the user terminal 110 the destination information DN and the route information RT that meet the category conditions along the selected logical method RN. At this time, as an example of how to select the recommended destination, select and recommend the one (the most effective one) selected by the most other users with the same user type tendency information TP. When the destination icon IC shown in FIG. 5 is selected by the user, as shown in FIG. 9, the recommendation means displays and recommends the destination information DN on the display unit 111 of the user terminal 110 along the selected logical method RN and visual expression method UI. Alternatively, among the ones selected (effective ones) by other users with the same user type tendency information TP or other users with the same tendency of the chart shape shown in FIG. 6(B), select and recommend the one with the highest increasing tendency in the most recent short period. When the user selects a category in the category selection operation determination step, within the range that meets the conditions of the category, select and recommend the one (the effective one) selected by many other users with the same user type tendency information TP.

[0084] The recommendation system 100, which is an embodiment of the present invention obtained in this way, includes a user terminal 110 and a server 120. When the current situation information ST of the user is input in the user terminal 110, in the user terminal 110 or the server 120, the recommendation means having artificial intelligence narrows down the destination information DN in consideration of the means of transportation information based on the previously input user preference information LK, the input current situation information ST of the user, and the current location information of the user terminal 110, and also narrows down the route information RT including the means of transportation information. By configuring to display the narrowed-down route information RT and the destination information DN as a set on the display unit 111 of the user terminal 110 for recommendation, the user can seamlessly move along with the recommendation presented by the recommendation means without thinking about "what should I do?", and if the user likes it, the user can obtain a new travel experience while shortening the travel time.

[0085] Furthermore, when the situation information ST includes weather and temperature information, and the current weather and temperature information is higher than the first predetermined temperature and hot, lower than the second predetermined temperature and cold, or it is rainy in the range including the current location and the destination, the recommendation means recommends the route information RT and the destination information DN that prioritize movement by bicycle or on foot with a roof over movement by bicycle or on foot without a roof, and movement by at least one of train, bus, car, and taxi. In this way, the user can move to the destination comfortably.

[0086] Also, when the situation information ST includes the user's fatigue degree information, and the current fatigue degree information is higher than a predetermined degree, the recommendation means recommends the route information RT and the destination information DN that prioritize movement by at least one of taxi and train over movement by bicycle and on foot. In this way, the user can move to the destination comfortably without performing a driving operation.

[0087] Also, when the situation information ST includes the user's calorie intake information and fatigue degree information, and the current fatigue degree information is lower than a predetermined degree and the calorie intake information is higher than a predetermined value, the recommendation means recommends route information RT and destination information DN that prioritize movement by at least one of a bicycle and walking over movement by a taxi and a train. As a result, the user can move to the destination while actively consuming calories.

[0088] Furthermore, when the situation information ST includes the user's baggage information and the current baggage information is more or heavier than a predetermined degree, the recommendation means recommends route information RT and destination information DN that prioritize movement by at least one of a bus, a train, a rental car, and a taxi over movement by a bicycle and walking. As a result, the user can comfortably move to the destination with little effort in carrying the baggage.

[0089] Also, when the situation information ST includes the user's electronic payment information until immediately before the current day, the recommendation means estimates at least one of the quantity and weight of the items the user has shopped for from the electronic payment information, and when it is determined that at least one of the estimated quantity and weight is more than a predetermined quantity or heavier than a predetermined weight, the recommendation means recommends route information RT and destination information DN that prioritize movement by at least one of a bus, a train, a rental car, and a taxi over movement by a bicycle and walking. As a result, the user can comfortably move to the destination with little effort in carrying the baggage.

[0090] Furthermore, the situation information ST includes information on whether the user is currently using a car or a motorcycle. When using a car or a motorcycle, the recommendation means recommends destination information DN and route information RT for destinations with a parking lot or destinations with a parking lot around them, and destinations that do not mainly serve alcohol. As a result, the user can park at the destination without difficulty in finding a place to park the car or motorcycle, and can further avoid the temptation of drinking and the risk of drunk driving afterwards.

[0091] In addition, the situation information ST includes the user's final destination information and information on the current use of a car or a motorcycle. When there is a final destination and the user is using a car or a motorcycle, the recommendation means recommends route information RT including parking lot information for waypoints and the movement of the car or motorcycle from the current location to the waypoint, and the movement of a train or bus from an intermediate destination to the final destination, as well as intermediate destination information DN. As a result, the user can perform so-called park-and-ride, changing from the movement of a car or a motorcycle to the movement of a train or bus, and can enjoy recommended experiences at intermediate destinations.

[0092] Furthermore, the recommendation means has a plurality of logical methods RN of recommendation nudges that prompt recommended actions. The logical methods RN of the plurality of recommendation nudges are logically different in the way they prompt. The recommendation means selects one of the logical methods RN of the plurality of recommendation nudges based on the user type tendency information TP obtained from the information input at the user terminal 110 or the history information of the user terminal 110 and the type-specific logical tendency information obtained from the effectiveness of the type-specific logical methods RN of the past user types. By displaying the destination information DN on the display unit 111 of the user terminal 110 and recommending it along the selected logical method RN, regardless of the user's preferences for known products, services, and places, the user will try to choose the recommended new destination and can be spontaneously induced by the user to act on things (potential preferences) that the user did not desire in the past, thus overcoming and correcting the bias in the user's actions.

[0093] Also, the program of the recommendation system 100 according to an embodiment of the present invention includes a situation information input determination step S3, which is an input determination step for the user terminal 110 or the server 120 to determine whether the current situation information ST of the user has been input at the user terminal 110, and a narrowing-down step S4 in which the recommendation means having artificial intelligence at the user terminal 110 or the server 120 narrows down the destination information DN in consideration of the means of movement information based on the previously input user preference information LK, the input current situation information ST of the user, and the current location information of the user terminal 110, and also narrows down the route information RT including the means of movement information. The user terminal 110 is provided with a recommendation display step S5 of displaying and recommending the narrowed-down route information RT and the destination information DN as a set on the display unit 111 of the user terminal 110. As a result, without thinking about "what should I do?", the user can seamlessly move along the recommendation presented by the recommendation means if they like it. Furthermore, the effect is significant, such as shortening the travel time and obtaining a new travel experience.

Explanation of Symbols

[0094] 100 ··· Recommendation System 110 ··· User Terminal 111 ··· Display Unit 112 ··· My Page Screen 113 ··· Your Mood Screen 114 ··· Recommendation Screen 120 ··· Server 121 ··· Database 130 ··· Store Terminal (Merchandise / Service / Location Provider Terminal) ST ··· Situation Information LK ··· Preference Information RT ··· Route Information DN ··· Destination Information IC ··· Icon RN ··· Logical Method (of Recommendation Nudge) UI ··· Visual Representation Method (of Recommendation User Interface) TP ··· User Type Tendency Information

Claims

1. A recommendation system comprising a user terminal and a server, which recommends a destination to a user, when user's current situation information is input in the user terminal, in the user terminal or the server, the recommendation means having artificial intelligence narrows down destination information in consideration of means of transportation information based on pre-input user preference information, the input user's current situation information, and the current location information of the user terminal, and also narrows down route information including means of transportation information, and displays and recommends the narrowed-down route information and destination information as a set on the display unit of the user terminal, at this time, the recommendation means has a plurality of logical methods of recommendation badges that prompt recommended actions, the logical methods of the plurality of recommendation badges are logically different in the way of prompting, the recommendation means is configured to select one from among the logical methods of a plurality of recommendation badges based on user type tendency information obtained from the information input in the user terminal or the user terminal's history information and type-specific logical tendency information obtained from the effectiveness of the type-specific logical methods of past user types, and display and recommend destination information on the display unit of the user terminal along the selected logical method. A recommendation system characterized by this.

2. the situation information includes weather and temperature information, when the current weather and temperature information is higher than the first predetermined temperature and hot, lower than the second predetermined temperature and cold, or the area including the current location and the destination is rainy, the recommendation means is more likely to move along a route with a roof by bicycle or on foot than to move along a route without a roof by bicycle and on foot, and recommends route information and destination information that prioritize movement by at least one of train, bus, car, and taxi. The recommendation system according to claim 1, characterized by this.

3. the situation information includes user fatigue degree information, when the current fatigue degree information is higher than a predetermined degree, the recommendation means recommends route information and destination information that prioritize movement by at least one of taxi and train over movement by bicycle and on foot. The recommendation system according to claim 1 or claim 2, characterized by this.

4. The situation information includes the user's calorie intake information and fatigue level information, When the current fatigue level information is lower than a predetermined level and the calorie intake information is higher than a predetermined value, the recommendation means recommends route information and destination information that prioritize movement by at least one of bicycle and walking over movement by taxi and train. The recommendation system according to any one of claims 1 to 3, characterized in that.

5. The situation information includes the user's baggage information, When the current baggage information is more or heavier than a predetermined level, the recommendation means recommends route information and destination information that prioritize movement by at least one of bus, train, rental car, and taxi over movement by bicycle and walking. The recommendation system according to any one of claims 1 to 4, characterized in that.

6. The situation information includes the user's electronic payment information until immediately before the current day, The recommendation means estimates at least one of the quantity and weight of the items purchased by the user from the electronic payment information, and when it is determined that at least one of the estimated quantity and weight is more than a predetermined quantity or heavier than a predetermined weight, the recommendation means recommends route information and destination information that prioritize movement by at least one of bus, train, rental car, and taxi over movement by bicycle and walking. The recommendation system according to any one of claims 1 to 5, characterized in that.

7. The situation information includes information on whether the user is currently using a car or a motorcycle, In the case of using a car or a motorcycle, the recommendation means recommends destination information and route information for a destination with a parking lot or a destination with a parking lot in its vicinity, and a destination that does not mainly serve alcohol. The recommendation system according to any one of claims 1 to 6, characterized in that.

8. The situation information includes the user's final destination information and information on whether the user is currently using a car or a motorcycle, There is a final destination, and in the case of using a car or a motorcycle, the recommendation means recommends parking lot information at a transit point, route information including the movement of the car or motorcycle from the current location to the transit point, the movement of a train or bus from an intermediate destination to the final destination, and intermediate destination information. The recommendation system according to any one of claims 1 to 7.

9. A program for a recommendation system that includes a user terminal and a server and recommends a destination to a user, An input determination step in which the user terminal or the server determines whether user's current situation information has been input at the user terminal, A narrowing-down step in which a recommendation means having artificial intelligence at the user terminal or the server narrows down destination information in consideration of means-of-movement information and narrows down route information including means-of-movement information based on pre-input user preference information, input user current situation information, and current location information of the user terminal, The recommendation means has a plurality of logical methods of recommendation nudges that logically prompt an action to be recommended in different forms, and based on user type tendency information obtained from the information input at the user terminal or the history information of the user terminal and type-specific logical tendency information obtained from the effectiveness of the type-specific logical methods of past user types, selects one from among the plurality of logical methods of recommendation nudges. A recommendation nudge selection step, A recommendation display step in which the user terminal displays and recommends a set of the narrowed-down route information and destination information on the display unit of the user terminal, and at this time, displays and recommends the destination information on the display unit of the user terminal in accordance with the selected logical method. A program for a recommendation system, characterized by causing the above to be executed.

Images