User Familiar Degree Aware Recommender System

In a recommender system, items can be rated across multiple fields by users with varying degrees of familiarity. Hence, the ratings in a recommender system should have different recommended weights. Ratings in fields where in the user has high or low familiarity should be given high or low recommended weights, respectively. However, current recommendation algorithms ignore this problem and use the ratings indiscriminately, thus affecting the accuracy of the recommendation system. In this paper, we provide a focused study of user-familiarity degree-aware recommendation and develop a user-familiarity degree-aware latent factor model for recommendations that considers both user familiarity and item features reflected by the tagging information. We also design a user-familiarity degree-aware probability matrix factorization model, which computes the degree of familiarity of a user with the items he/she has rated. By using the user-familiarity degree, different recommended weights are given to every rating to obtain precise recommendations. The experiment results on real-world datasets show that our algorithm significantly outperforms state-of-the-art latent factor models and effectively improves the accuracy of the recommendation results.