Friend recommending method in position social network based on positions and time

Abstract

The invention discloses a friend recommending method in a position social network based on positions and time. The method mainly is used for solving the problem of low recommending precision resulting from the face that the existing friend recommending scheme ignores time information and sign-in position semantic information in sign-in information. The method comprises following steps of 1, building a communication system framework composed of users, positioning facilities and a position server; 2, sending the longitudes and latitudes of the positions and current time information to a social network server by the users, thus generating the sign-in information; 3, preprocessing massive stored sign-in information by the social network server; 4, calculating the word frequency-inverse document frequency value of each user to each place class by the social network server; 5, calculating similarities among the users by the social network server; and recommending friends to the users with relatively high similarities. According to the method, the available data sparsity of a recommending system is mitigated; the recommending precision is improved; and the method can be applied in the wireless social network service based on the positions.

Description

Technical field: [0001] The invention belongs to the technical field of wireless networks, relates to friend recommendation in wireless social networks, and can be applied to location-based wireless social network services. Background technique: [0002] Location-based social networks help users share their real-time locations online so that users can discover places of interest and make friends. For example, users can discover places they are interested in through the locations shared by their friends, or make new friends by finding users who share similar places with them. In addition, with the popularity of smart phones, their built-in GPS modules can detect the user's location more accurately, so that users can share their respective locations more conveniently. Therefore, this check-in service attracts more and more users. How to use a large amount of check-in information to recommend friends for users needs to be paid attention to. [0003] Recommender systems play ...

AI Technical Summary

Problems solved by technology

[0007] The present invention solves the problem of sparse data available in the recommendation system by classifying places with similar semantic information into one category, uses word frequency-inverse document frequency to balance popular places and users' own interests, and analyzes the distribution of user check-in behavior over time According to the rules, adjust the similarity calculation of users to achieve more accurate friend recommendation

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