Recommender systems have a unique role in on-line trading companies due to building relationships among users and items to reduce big information load. There exist several successful algorithms in the recommender systems like collaborative filtering (CF), although most of them suffer from the sparsity problem. Here, we propose a novel integrated recommendation approach based on the tools of network science to mitigate the sparsity problem. The link prediction approach is used to extract hidden structures among users, and diffusion of information is applied to enhance the rating matrix in our proposed framework. Not only, the sparsity problem is alleviated through a more efficient way, but the proposed approach also can be applied in a hybrid way with the well-known algorithms. The proposed approach is examined on several datasets via standard evaluation criteria. The experimental results show that the proposed approach outperforms the earlier methods.
