  Hybrid recommendation usually combines collaborative filtering with
content-based filtering to exploit merits of both techniques. It is widely
accepted that hybrid filtering outperforms the single algorithm, thus it has
been the new trend in electronic commerce these years. In this paper, we
propose a novel hybrid recommendation system based on weighted stochastic block
model (WSBM). Our algorithm not only makes full use of content-based and
collaborative filtering recommendation to solve the cold-start problem but also
improves the accuracy of recommendation by selecting the nearest neighbor with
WSBM. The experiment result shows that our proposed approach has better
prediction and classification accuracy than traditional hybrid recommendation.
