A collaborative filtering algorithm based on social network information

In traditional collaborative filtering recommendation, the matrix sparsity and cold start restricted the accuracy of system. In this paper, we develop a way to enhance the recommendation effectiveness by merging neighborhood relationship and users keyword of social network information into collaborative filtering. We extend the calculation method of the TOP N neighbors which is the most important from two aspects. Our method expands the information capacity which can be used by collaborative filtering, improves the accuracy of recommendation and eases the cold start problem in recommendation system. We conducts experiment based on KDD 2012 real data set. The result indicates that our algorithm performs more superior than traditional collaborative filtering algorithm.