A system for creating recommendations based on location data

Abstract

The present invention relates to a system (1) which enables to provide personalized recommendation by modeling a mobile user's historical location information and the time s / he spends in these locations by means of machine learning techniques.

Description

[0001] A SYSTEM FOR CREATING RECOMMENDATIONS BASED ON LOCATION DATA

[0002] Technical Field

[0003] The present invention relates to a system which enables to provide personalized recommendation by modeling a mobile user’s historical location information and the time s / he spends in these locations by means of machine learning techniques.

[0004] Background of the Invention

[0005] Today, there are many organizations which provide personalized advertising and recommendation services based on location information. There are many large companies providing vacation, food and place recommendations. In these organizations, dining and vacation services that may attract the users’ interest are recommended in locations close to their current location. In addition to instant location information, such organizations also make use of other information such as the user’s internet search history. Social media companies also provide recommendations of new social networks, news and places based on the user’s social network and search history. However, there is no arrangement such as determining a user’s interests by performing a more comprehensive profile analysis based on the regular location pattern created by the user’s routine stops and providing appropriate recommendation services.

[0006] Therefore, considering the studies and the shortcomings included in the current technique, it is understood that there is need for a system for providing recommendations which may facilitate persons’ lives and improve their life quality by predicting the user’s interest by means of machine learning algorithms. In the United States patent document no. US20130066821A1, an application included in the state of the art, the popularity of a social environment and venue are taken into account and it is presented to the user via a mobile application. Here, the user’s instant location information is used to list the recommendations of popular venues in the nearest location. In this study, the mobile application developed has the capability to determine the places visited by the user and his / her connections in his / her social network and which places these associated users want to visit. However, the United States patent document no. US20130066821A1 does not provide solutions such as extracting the user’s regular movement pattern by taking into account the user’s past location information and the time spent in these locations and processing it by means of machine learning techniques in order to create a comprehensive user profile suitable for the user’s social and daily life, as well as developing a personal recommendation service such as vacation, venue, cultural event, etc. in order to help organize the user’s social life in accordance with this user profile by using a data set consisting of online reviews and business data.

[0007] Summary of the Invention

[0008] An objective of the present invention is to realize a system which is developed for providing personalized recommendation by modeling a mobile user’s historical location information and the time s / he spends in these locations by means of machine learning techniques.

[0009] Another objective of the present invention is to realize a system which is developed for obtaining a regular location pattern of a user by means of machine learning algorithms by using the data about the user’s past location information, routine frequent destinations and the time s / he at these points; predicting a user’s profile in the light of this data; and providing a recommendation service based on online reviews and business data that are appropriate for this user location pattern. Another objective of the present invention is to realize a system which is developed for providing a recommendation service based on a user’s Pol (Point of Interest) that is estimated based on a regular location pattern.

[0010] Detailed Description of the Invention

[0011] “A System for Creating Recommendation Based on Location Data” realized to fulfil the objective of the present invention is shown in the figure attached, in which:

[0012] Figure l is a schematic view of the inventive system.

[0013] The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0014] 1. System

[0015] 2. Mobile Device

[0016] 3. Recommendation Database

[0017] 4. Database Server

[0018] 5. Location Data Analysis Server

[0019] 6. Server for Creating Regular Location Model

[0020] 7. Server for Determining U ser’ s Interests

[0021] 8. Server for Predictive Analytics Pol Modeling

[0022] 9. Server for Making Personalized Recommendation

[0023] 10. Feedback Server

[0024] H. Map

[0025] The inventive system (1) developed for providing recommendations based on location data, comprises at least one mobile device (2) which is configured to exchange data using any remote communication protocol and to execute at least one application thereon; at least one recommendation database (3) which is configured to keep record of online reviews and business data therein; at least one database server (4) which is configured to establish connection with the mobile device (2) by using any communication protocol; to process the location data; and to keep record of the user’s historical location data, that is accessed over the cellular network via the mobile device (2) connected to the GNSS, with the user’s permission; at least one location data analysis server (5) which is configured to establish connection with the database server (4) by using any communication protocol; to access the historical location data kept on the database server (4) and the data retrieved from the map (H); and then to make sense of the time spent by the user at these locations; at least one server for creating regular location model (6) which is configured to establish connection with the location data analysis server

[0026] (5) by using any communication protocol; to analyze the meaningful data received via the location data analysis server (5) by means of machine learning algorithms; at least one server for determining user’s interests (7) which is configured to establish connection with the server for creating regular location model

[0027] (6) by using any communication protocol; to receive the user’s regular location pattern generated on the server for creating regular location model (6) as input; to keep record og it; to create a comprehensive user profile of the user’s social and daily life; and to obtain user Pols (Point of Interest); at least one server for predictive analytics Pol modeling (8) which is configured to establish connection with the server for determining user’s interests (7) by using any communication protocol; to access the user Pol information in the server for determining user’s interests (7) and the recommendation repository in the recommendation database (3); and then to predict recommendations in accordance with the user’s Pol; at least one server for making personalized recommendation (9) which is configured to establish connection with the server for predictive analytics Pol modeling (8) by using any communication protocol; to present the recommendations predicted to be appropriate to the user Pol in the server for predictive analytics Pol modeling (8) to the user on the mobile device (2) by means of a personalized recommendation algorithm; and at least one feedback server (10) which is configured to create a feedback mechanism by tracking the user’s reactions to the recommendations presented upon establishing connection with the server for making personalized recommendation (9) by using any communication protocol; and to transmit it to the server for determining user’s interests (7) for further refinement of the Pol determined on server for determining user’s interests (7).

[0028] The mobile device (2) included in the inventive system (1) is configured to realize data exchange by using any communication protocol included in the state of the art and to execute at least one application thereon. The mobile device (2) is a device such as a mobile phone. The mobile device (2) is configured to establish connection with the database server (4) and the server for making personalized recommendation (9) by using any remote communication protocol included in the state of the art.

[0029] The recommendation database (3) included in the inventive system (1) is configured to establish connection with the server for predictive analytics Pol modeling (8). The recommendation database (3) is configured to keep record of online reviews and business data therein.

[0030] The database server (4) included in the inventive system (1) is configured to establish connection with the mobile device (2) by using any communication protocol included in the state of the art. The database server (4) is configured to establish connection with the location data analysis server (5) by using any communication protocol included in the state of the art. The database server (4) is configured to keep record of the historical location data of the user, that is accessed over the cellular network via the mobile device (2) connected to the GNSS (Global Navigation Satellite Systems), with the user’s permission,

[0031] The location data analysis server (5) included in the inventive system (1) is configured to establish connection with the database server (4) and the server for creating regular location model (6) by using any communication protocol included in the state of the art. The location data analysis server (5) is configured to access historical location data kept on the database server (4) and the data retrieved from the map (H) and then to make sense of the time spent by the user at these locations.

[0032] The server for creating regular location model (6) included in the inventive system (1) is configured to establish connection with the location data analysis server (5) and the server for determining user’s interests (7) by using any communication protocol included in the state of the art. The server for creating regular location model (6) is configured to analyze the meaningful data received via the location data analysis server (5) by means of machine learning algorithms.

[0033] The server for determining user’s interests (7) included in the inventive system (1) is configured to establish connection with the server for creating regular location model (6) by using any communication protocol included in the state of the art. The server for determining user’s interests (7) is configured to establish connection with the feedback server (10) by using any communication protocol included in the state of the art. The server for determining user’s interests (7) is configured to receive the user’s regular location pattern generated on the server for creating regular location model (6) as input and then to keep record of it; to create a comprehensive user profile of the user’s social and daily life; and to obtain user Pols (Point of Interest). The server for determining user’s interests (7) is configured to establish connection with the predictive analytical Pol modeling server (8) and transmit the user Pols using any communication protocol in the known state of the art. The user interests identification server (7) is configured to establish connection with the feedback server (10) by using any communication protocol included in the state of the art to enable more precise acquisition of user Pols.

[0034] The server for predictive analytics Pol modeling (8) included in the inventive system (1) is configured to establish connection with the recommendation database (3), the server for determining user’s interests (7) and the server for making personalized recommendation (9) by using any communication protocol included in the state of the art. The server for predictive analytics Pol modeling (8) is configured to access the recommendation repository from the recommendation database (3) consisting of the server for determining user’s interests (7), the user Pol from the map (H) and the online reviews, business data, and to predict recommendations that match the user's Pol.

[0035] The server for making personalized recommendation (9) included in the inventive system (1) is configured to establish connection with the server for predictive analytics Pol modeling (8) and the feedback server (10) by using any communication protocol included in the state of the art. The server for making personalized recommendation (9) is configured to present the recommendations estimated to be appropriate to the user Pol in the server for predictive analytics Pol modeling (8) to the user on the mobile device (2) via the customized recommendation algorithm.

[0036] The feedback server (10) included in the inventive system (1) is configured to establish connection with the server for determining user’s interests (7) and server for making personalized recommendation (9) by using any communication protocol included in the state of the art. The feedback server (10) is configured to generate a feedback mechanism by monitoring the user's reactions to the recommendations presented to the user and transmitting it to the the server for determining user’s interests (7) for further refinement of the Pol determined at the the server for determining user’s interests (7).

[0037] Industrial Application of the Invention

[0038] In the inventive system (1), the server for making personalized recommendation (9) establishes a connection with the server for predictive analytics Pol modeling

[0039] (8) and the feedback server (10) by using any communication protocol included in the known state of the art. The server for making personalized recommendation

[0040] (9) provides the user on the mobile device (2) with the recommendations predicted to be appropriate to the user's Pol from the server for predictive analytics Pol modeling (8) through the personalized recommendation algorithm. The feedback server (10) monitors the user's reactions to the recommendations presented to the user and transmits them to the server for determining user’s interests (7) to create a feedback mechanism and to further refine the Pol determined in the server for determining user’s interests (7). Thus, a recommendation service is provided according to the user Pol (Point of Interest) estimated based on the regular location pattern.

[0041] Within these basic concepts; it is possible to develop various embodiments of the inventive “System (1) for Creating Recommendation Based on Location Data”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system (1) developed for providing recommendations based on location data, comprising at least one mobile device (2) which is configured to exchange data using any remote communication protocol and to execute at least one application thereon; at least one recommendation database (3) which is configured to keep record of online reviews and business data therein; and characterized by at least one database server (4) which is configured to establish connection with the mobile device (2) by using any communication protocol; to process the location data; and to keep record of the user’s historical location data, that is accessed over the cellular network via the mobile device (2) connected to the GNSS, with the user’s permission; at least one location data analysis server (5) which is configured to establish connection with the database server (4) by using any communication protocol; to access the historical location data kept on the database server (4) and the data retrieved from the map (H); and then to make sense of the time spent by the user at these locations; at least one server for creating regular location model (6) which is configured to establish connection with the location data analysis server (5) by using any communication protocol; to analyze the meaningful data received via the location data analysis server (5) by means of machine learning algorithms; at least one server for determining user’s interests (7) which is configured to establish connection with the server for creating regular location model (6) by using any communication protocol; to receive the user’s regular location pattern generated on the serverfor creating regular location model (6) as input; to keep record og it; to create a comprehensive user profile of the user’s social and daily life; and to obtain user Pols (Point of Interest); at least one server for predictive analytics Pol modeling (8) which is configured to establish connection with the server for determining user’s interests (7) by using any communication protocol; to access the user Pol information in the server for determining user’s interests (7) and the recommendation repository in the recommendation database (3); and then to predict recommendations in accordance with the user’s Pol; at least one server for making personalized recommendation (9) which is configured to establish connection with the server for predictive analytics Pol modeling (8) by using any communication protocol; to present the recommendations predicted to be appropriate to the user Pol in the server for predictive analytics Pol modeling (8) to the user on the mobile device (2) by means of a personalized recommendation algorithm; and at least one feedback server (10) which is configured to create a feedback mechanism by tracking the user’s reactions to the recommendations presented upon establishing connection with the server for making personalized recommendation (9) by using any communication protocol; and to transmit it to the server for determining user’s interests (7) for further refinement of the Pol determined on server for determining user’s interests (7).

2. A system (1) according to Claim 1; characterized by the mobile device (2) which is configured to realize data exchange by using any communication protocol included in the state of the art and to execute at least one application thereon.

3. A system (1) according to Claim 1 or 2; characterized by the mobile device (2) which is configured to establish connection with the database server (4) and the server for making personalized recommendation (9) by using any remote communication protocol.

4. A system (1) according to any of the preceding claims; characterized by the recommendation database (3) which is configured to establish connection with the server for predictive analytics Pol modeling (8).

5. A system (1) according to any of the preceding claims; characterized by the recommendation database (3) which is configured to keep record of online reviews and business data therein.

6. A system (1) according to any of the preceding claims; characterized by the database server (4) which is configured to establish connection with the mobile device (2) by using any communication protocol.

7. A system (1) according to any of the preceding claims; characterized by the database server (4) which is configured to establish connection with the location data analysis server (5) by using any communication protocol.

8. A system (1) according to any of the preceding claims; characterized by the database server (4) which is configured to keep record of the historical location data of the user, that is accessed over the cellular network via the mobile device (2) connected to the GNSS (Global Navigation Satellite Systems), with the user’s permission.

9. A system (1) according to any of the preceding claims; characterized by the location data analysis server (5) which is configured to establishconnection with the database server (4) and the server for creating regular location model (6) by using any communication protocol.

10. A system (1) according to any of the preceding claims; characterized by the location data analysis server (5) which is configured to access historical location data kept on the database server (4) and the data retrieved from the map (H) and then to make sense of the time spent by the user at these locations.

11. A system (1) according to any of the preceding claims; characterized by the server for creating regular location model (6) which is configured to establish connection with the location data analysis server (5) and the server for determining user’s interests (7) by using any communication protocol.

12. A system (1) according to any of the preceding claims; characterized by the server for creating regular location model (6) which is configured to analyze the meaningful data received via the location data analysis server (5) by means of machine learning algorithms.

13. A system (1) according to any of the preceding claims; characterized by the server for determining user’s interests (7) which is configured to establish connection with the server for creating regular location model (6) by using any communication protocol.

14. A system (1) according to any of the preceding claims; characterized by the server for determining user’s interests (7) which is configured to establish connection with the feedback server (10) by using any communication protocol.

15. A system (1) according to any of the preceding claims; characterized by the server for determining user’s interests (7) which is configured to receive the user’s regular location pattern generated on the server for creating regular location model (6) as input and then to keep record of it; to create a comprehensive user profile of the user’s social and daily life; and to obtain user Pols (Point of Interest).

16. A system (1) according to any of the preceding claims; characterized by the server for determining user’s interests (7) which is configured to establish connection with the predictive analytical Pol modeling server (8) and transmit the user Pols using any communication protocol.

17. A system (1) according to any of the preceding claims; characterized by the user interests identification server (7) which is configured to establish connection with the feedback server (10) by using any communication protocol included in the state of the art to enable more precise acquisition of user Pols.

18. A system (1) according to any of the preceding claims; characterized by the server for predictive analytics Pol modeling (8) which is configured to establish connection with the recommendation database (3), the server for determining user’s interests (7) and the server for making personalized recommendation (9) by using any communication protocol.

19. A system (1) according to any of the preceding claims; characterized by the server for predictive analytics Pol modeling (8) which is configured to access the recommendation repository from the recommendation database (3) consisting of the server for determining user’s interests (7), the user Pol from the map (H) and the online reviews, business data, and to predict recommendations that match the user's Pol.

20. A system (1) according to any of the preceding claims; characterized by the server for making personalized recommendation (9) which is configured to establish connection with the server for predictive analytics Pol modeling (8) and the feedback server (10) by using any communication protocol.

21. A system (1) according to any of the preceding claims; characterized by the server for making personalized recommendation (9) which is configured to present the recommendations estimated to be appropriate to the user Pol in the server for predictive analytics Pol modeling (8) to the user on the mobile device (2) via the personalized recommendation algorithm.

22. A system (1) according to any of the preceding claims; characterized by the feedback server (10) which is configured to establish connection with the server for determining user’s interests (7) and server for making personalized recommendation (9) by using any communication protocol.

23. A system (1) according to any of the preceding claims; characterized by the feedback server (10) which is configured to generate a feedback mechanism by monitoring the user's reactions to the recommendations presented to the user and transmitting it to the the server for determining user’s interests (7) for further refinement of the Pol determined at the the server for determining user’s interests (7).

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