Research On The Algorithm For Friend Recommendation In Location-based Social Networks

Friend recommendation algorithm, as one of the main means to recommend potential friends for user in social networks, which can improvement the precision of friend recommendation, help users to develop their social behavior with potential friends, to ensure the user’s activity in the social network and improve the user viscosity. In recent years, the social network service has integration with Location Based Service which growing popularity, and formed the Location Based Social Network. The user’s relationship with friends and social behavior has a strong correlation between geographical positions. But the traditional friend recommendation algorithms ignore the location information in the process of produce recommendation results.This paper mining the data of Brightkite, one of the Location Based Social Network,analyze the user behaviors of check-in and found the user location information’s impact on friend network topology. Then, according to the influence, this paper gives an improvement to the traditional classic recommendation algorithms. And it demonstrate the improved algorithms can improve the accuracy of friend recommendation results using location information by friends recommend experiments, without significantly increase the algorithms’ time complexity at the same time. Finally, it further mining the check-in time information’s deep influence on the relationship between friends. And it tries to introduce time information into the algorithms’ improvement, to exaltation the accuracy of friends recommendation algorithms.