With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users’ individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space.we present the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and, more importantly, performs well on the large, sparse, and very imbalanced Netflix dataset. We further extend the PMF model to include an adaptive prior on the model parameters and show how the model capacity can be controlled automatically. we achieve an error rate of 0.8861, that is nearly 7% better than the score of Netflix’s own system.If recommender systems have established their key role in providing the user access to resources on the web, when sharing resources has turn into social, it is likely for recommendation techniques in the social web should consider social popularity factor and the relationships among users to compute their predictions. In this paper contextual information are being included in social popularity based SVD++ model to improve accuracy and scalability of recommendations. we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative filtering framework. iExpand strategically deals with many issues that exist in traditional collaborative filtering approaches, such as the overspecialization problem and the cold-start problem. The cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space.