Friend Recommendation Algorithm Based on User Activity and Social Trust in LBSNs

In LBSNs (Location-based Social Networks), friend recommendation results are mainly decided by the number of common friends or depending on similar user preferences. However, lack of description of semantic information about user activity preferences, insufficiency in building social trust among user relationships and individual score ranking by a crowd or the person from third party of social networks make recommendation quality undesirable. Aiming at this issue, FRBTA algorithm is proposed in this paper to recommend best friends by considering multiple factors such as user semantic activity preferences, social trust. Experimental results show that the proposed algorithm is feasible and effective.