A Point-of-Interest Recommendation Algorithm Combining Social Influence and Geographic Location Based on Belief Propagation

Abstract

The location-based social network (LBSN) contains a large amount of user check-in data informations, in order to better improve the recommendation performance and avoid the impact of user check-in data sparsity. It is proposed to mine the time-category informations in the user's check-in data to carry out network modeling; by using the belief propagation algorithm on the time-category Markov network to obtain the user's social influence set. Calculating the similarity and familiarity of the social users in the collection, linearly integrate the unified social influence factors and geographical location influences, to recommend locations. Experimental analysis in the Foursquare dataset, compared with other algorithms, the recommendation algorithm performance of combining social influence and geographic location based on belief propagation has improved.