SimRate: Improve Collaborative Recommendation Based on Rating Graph for Sparsity

Collaborative filtering is a widely used recommending method. But its sparsity problem often happens and makes it defeat when rate data is too few to compute the similarity of users. Sparsity problem also could result into error recommendation. In this paper, the notion of SimRank is used to overcome the problem. Especially, a novel weighted SimRank for rate bi-partite graph, SimRate, is proposed to compute similarity between users and to determine the neighbor users. SimRate still work well for very sparse rate data. The experiments show that SimRate has advantage over state-of-the-art method.