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Mode and preference sensing POI recommendation method and system

A mode, mobile mode technology, applied in the field of network communication, can solve the problems of low scalability, POI recommendation system does not consider the order of user access location, cannot effectively implement POI recommendation, etc., to improve scalability, solve location problems, etc. Effects of Finiteness and Data Sparsity Problems

Inactive Publication Date: 2016-10-12
BEIJING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] First, considering only geographical location information cannot effectively implement POI recommendation, and there are often data sparsity problems in the actual environment; second, the existing POI recommendation system does not consider the sequence of user access locations. For users, different The order of visits will produce different interests and preferences; third, the existing POI recommendation system cannot adapt to different types of POI classification well, so the scalability is low

Method used

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  • Mode and preference sensing POI recommendation method and system
  • Mode and preference sensing POI recommendation method and system
  • Mode and preference sensing POI recommendation method and system

Examples

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Embodiment 1

[0056] Such as figure 1 As shown, it is a flow chart of the principle of the mode and preference-aware POI recommendation method provided by the embodiment of the present invention, wherein,

[0057] Step 10, convert the geographic location information in the GPS data set into semantic information by using the semantic trajectory conversion method.

[0058] In a location-based social network, each user has the geographical location information of their check-in. Using this information, we can predict the user's future activity location and recommend point-of-interest (POI)-based services for the user. However, due to the limited geographic location and data sparsity, we cannot mine similar users well from the original geographic location information. Such as figure 2 As shown in , it represents three mobile trajectories (A, B, C) generated by three users, the left half is the location trajectory, and the right half is the corresponding semantic trajectory. When comparing ...

Embodiment 2

[0114] Such as Figure 5 As shown, the embodiment of the present invention provides a POI recommendation system of mode and preference perception, including a semantic trajectory conversion module 100, a path extraction module 200, a movement pattern extraction module 300, a potential friend discovery module 400, a candidate service mining module 500 and POI recommends module 600, as follows:

[0115] Semantic trajectory conversion module 100, for converting the geographic location information in the location-based social network data set into semantic information;

[0116] The path extraction module 200 is used to extract the location path and semantic path of each user;

[0117] The mobile pattern extraction module 300 is used to extract the mobile pattern of each user;

[0118] A potential friend discovery module 400, configured to discover a potential friend set of the target user;

[0119]A candidate service mining module 500, configured to mine candidate services suit...

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Abstract

The invention discloses a mode and preference sensing POI recommendation method. The method includes: converting geographical location information of GPS data centralization into semantic information; considering the location popularity and the user familiarity; establishing a model for moving behaviors of a user; finding a potential friend of a target use; mining a candidate service from a behavior model of the potential friend; scoring the candidate service; and recommending first K candidate services to the target user. The method can achieve personalized POI recommendation, can solve the problem of position finiteness and data sparsity in an actual environment, and reflect interests and preferences of the users through a movement path description method, improve the extendibility of the system, and provide a beneficial solution idea for location based social network personalized route recommendation in future.

Description

technical field [0001] The present invention relates to the field of network communication, in particular to a mode and preference-aware POI recommendation method and system. Background technique [0002] Location-based social networks (LBSNs) (for example: Foursquare, FacebookPlace, Twitter, Streetside, etc.) use the user's check-in information to combine online social networks and physical locations to share location-based service resources in the virtual world. and spread. In recent years, due to the widespread application of smart mobile devices embedded with a large number of sensors, LBSNs have achieved unprecedented development. Among them, smartphones have undoubtedly become the most popular devices for users. Users can access the Internet everywhere through smart phones, and use location-based applications to experience various network services, such as multimedia services, real-time news, and traffic information. Nokia predicts that with the sharp increase in th...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9537
Inventor 许长桥关建峰朱亮
Owner BEIJING UNIV OF POSTS & TELECOMM
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