An enhanced social matrix factorization model for recommendation based on social networks using social interaction factors

Recommender systems are recently becoming more significant in the age of rapid development of Internet technology and pervasive computing due to their ability in making appropriate choices to users. Collaborative filtering is one of the most successful recommendation techniques, which recommends items to an active user based on past ratings from like-minded users. However, the user-item rating matrix, namely one of the inputs to the recommendation algorithm, is often highly sparse, thus collaborative filtering may lead to the poor recommendation. To solve this problem, social networks can be employed to improve the accuracy of recommendations. Some of the social factors have been used in recommender system, but have not been fully considered. In this paper, we fuse personal cognition behavior, cognition relationships between users, and time decay factor for rated items into a unified probabilistic matrix factorization model and propose an enhanced social matrix factorization approach for personalized recommendation using social interaction factors. In this study, we integrate propagation enhancement, common user relationship enhancement, and common interest enhancement into social relationship between users, and propose a novel trust relationship calculation to alleviate the negative impact of sparsity of data rating. The proposed model is compared with the existing social recommendation algorithms on real world datasets including the Epinions and Movielens datasets. Experimental results demonstrate that our proposed approach achieves superior performance to the other recommendation algorithms.