An improved collaborative filtering algorithm based on bipartite network

Recommender System is one of the most important technologies in E-commerce, and the collaborative filtering algorithm is the most widely used technique. In this paper, we proposed an improved collaborative filtering algorithm based on bipartite network, degree of nodes and sort of nodes both have been taken into account. And we only need to calculate the top-N similar neighbors for each target item, which take less reaction time. Based on the MovieLens data set the experimental results demonstrate that the algorithm is better than the standard Pearson and Cosine correlation both in the accuracy and computation time.