Study On Potential Friends Recommender System Of Location-Based Social Networks

While the LBS(Location-Based Service) and social networks gradually mix together, LBSNs (Location-Based Social Networks) has emerged. And with the number of users and check-ins in LBSNs continue to increase, the introduction of recommender systems which has the ability of information filtering in LBSNs will better help users shorten the time to find the contents they really interested in, and improve the efficiency of getting their requirement.This paper mainly research the potential friends recommender systems which is one of the aspects in LBSNs recommendation technology. Through the study of LBSNs’hierarchical network structure, similarity calculating methods and some friend recommender algorithms, comparing their merit and demerit, this paper presented a new friends recommender algorithm based on social relationship and check-in behavior, and use it in a prototype design and implement of a potential friend recommender system in order to promote the effect of recommending potential friends. The specific research works are as follows:Aimed at the problem that traditional friend recommender algorithms in LBSNs do not have a better way to analyze social relationship, this paper presented a new friends recommender algorithm based on social relationship and check-in behavior called PFRSC algorithm. In the consideration of social relationship, according to the common friends of the target user and its neighbor node, PFRSC first computes the direct relationship value of each other, and then using the relationship transitivity to calculate the similar user set in social relationship for target user, it can better represent the intensity of social relationship.When computing the check-in behavior similarity between users, this paper presented a new approach based on check-in frequency and check-in ratio, which normalization process for common check-in times, comprehensively considerate user’s personal preference and public preference to calculate the check-in behavior similarity, it can solve the problems in traditional algorithms such as only considering the number of common check-in locations without considering the common check-in times, better improve the efficiency of finding potential friends. Last, using the precision and recall as the measurement approach for recommending effect, the experiment shows that PFRSC has a better recommending effect than the traditional friends recommending algorithms.Based on the PFRSC algorithm, through the investigation and requirement analysis of potential friends recommender system, this paper has designed and implemented a potential friends recommender system prototype which including architectural structure, functional module and database design etc. The system can predict the potential friends for the current user, ranking the comprehensive similarity of social relationship and check-in behavior from high score to low score, finally shows a friend recommender list to the user, providing a reasonable basis to make a choice, helping the user to establish and expand their social circle.