Ftmf: Recommendation In Social Network With Feature Transfer And Probabilistic Matrix Factorization

It is well-known that recommendation system which is widely used in many e-commerce platforms to recommend items to the right users suffers from data sparsity, imbalanced rating and cold start problems. Matrix factorization is a good way to deal with the sparsity and imbalance problems, which is however unable to make prediction for new users due to the lack of auxiliary information. With the advent of online social networks, the trust relation in the social network can be utilized as auxiliary data to solve the aforementioned problems since consumers' buying behavior is usually affected by the people around them. This paper reports a study of exploiting the trust relationship in social network for personalized recommendation. Although previous studies have paid attention to this topic, we improve the quality of recommendation further and solve the cold start problem better. To this end, we propose a recommendation system in social network with Feature Transfer and Probabilistic Matrix Factorization (FTMF). The auxiliary data and matrix factorization technique are integrated to learn a social latent feature vector of users which represents the features transferred from trusted people. And an adaptive firm factor is introduced to balance the impact from user's own factors and trusted people on buying behavior for each user. The experimental results show that our model can effectively use the auxiliary data and outperforms the existing state-of-the-art social network based recommendation algorithms.