Abstract: Recently,collaborative filtering is one of the most widely used and successful recommendation technology in recommender system.And probabilistic matrix factorization is an important method of collaborative filtering and it can be recommended by learning the low dimensional approximation matrix.However,the traditional collaborative filtering recommendation algorithm has the disadvantages of using the ratings between users and items only,ignoring the potential impact of the users (items).At last,it affects the recommendation precision.In order to solve the problem,in this paper,we first used the Jaccard similarity to preprocess the users (items),and then dug out the potential impact through the users (items) location information,finding the set of nearest neighbors successfully.Furthermore,those nearest neighbors were successfully applied into the recommendation process based on probabilistic matrix factorization.Experimental results show that compared to traditional collaborative filtering recommendation algorithm,the proposed algorithm can achieve more accurate rating predictions and improve the quality of recommendation.

Key words: Jaccard similarity,Locational behaviors,Collaborative filtering,Probabilistic matrix factorization