Social recommendation algorithm fusing user interest social network

Using the social information among users in recommender system can partly solve the data sparsely problems and significantly improve the performance of the recommendation system. However, the recommendation systems which using the users' social information have two main problems: the explicit user social connection information is not always available in real-world recommender systems, and the user social connection information is directly used in recommender systems when the user explicit social information is available. But as we know that the user social information is not all based on user interest, so this can introduce noise to the recommender systems. This paper proposes a social recommender system model called interest social recommendation (ISoRec). Based on probability matrix factorization (PMF), the model addresses the problems mentioned above by combining user-item rating matrix, explicit user social connection information and implicit user interest social connection information to make more accurately recommendation. In addition, the computational complexity of our algorithm is linear with respect to the number of observed data sets used in this algorithm, and can scalable to very large datasets.