Refine social relations and differentiate the same friends’ influence in recommender system

Abstract

Social relations has been widely used in recommender system to improve accuracy of recommendations. Most works consider influence from overall friends simultaneously when recommending, and to each item the same friend always has equal influence. However, existing models fail to be consistent with real life recommendations, because in real life only a part of friends can affect our decisions, and we couldn’t be influenced by the same friends on everything. So in this paper, we use machine learning way to infer truly influential friends in a mixed friends circle. And to different items we use relevance to differentiate the same friend’s influence. A model, Topic-based Friends Refining Probabilistic Matrix Factorization (TFR-PMF), is proposed to check the performance of our theory. Through experiments on public data set, we domenstrate that our method can increase the accuracy of recommendation by 6.5%, comparing with models that do not filter unrelated friends’ influence.