Graph-Aware Collaborative Filtering for Top-N Recommendation
Lianjie Long, Yunfei Yin, Faliang Huang · 2021
Recommender systems based on collaborative filtering has always suffered from sparsity and cold start problems. Therefore, researchers attempt to address the issues with various side information such as user profiles and item attributes. In this paper, we proposed Graph-aware Collaborative Filtering (GCF), an end-to-end framework, in which user-item bipartite graph and the knowledge graph of items (side information) are integrated to improve recommendation performance. In GCF, we aggregate the neighbors in the candidate item knowledge graph to refine the item representation. Similarly, we aggregate the user interaction neighbors in the user-item bipartite graph to refine the user representation. The collaborative signals in the knowledge graph and user-item bipartite graph are successfully captured through the neighborhood aggregation operation. Experimental results on three real datasets indicate that the proposed GCF is superior to the existing models in terms of accuracy and can also effectively solve the data sparsity problem of the recommender system.