A Negative-Aware and Rating-Integrated Recommendation Algorithm Based on Bipartite Network Projection

Bipartite network projection method has been recently employed for personal recommendation. It constructs a bipartite network between users and items. Treating as resource in the network user taste for items, it allocates the resource via links between user nodes and item nodes. However, the taste model employed by existing algorithms cannot differentiate "dislike" and "unrated" cases implied by user ratings. Moreover, the distribution of resource is solely based on node degrees, ignoring the different transfer rates of the links. To enhance the performance, this paper devises a negative-aware and rating-integrated algorithm on top of the baseline algorithm. It enriches the current user taste model to encompass "like", "dislike" and "unrated" information from users. Furthermore, in the resource distribution stage, we propose to initialize the resource allocation according to user ratings, which also determines the resource transfer rates on links afterward. Extensive experiments conducted on real data validate the effectiveness of the proposed algorithm.