GSIRec: Learning with graph side information for recommendation

Collaborative filtering (CF) is one of the dominant techniques used in modern recommender systems. Traditional CF-based methods suffer from issues of data sparsity and cold start. Therefore, side information has been widely utilized by researchers to address these problems. Most side information is typically heterogeneous and in the form of the graph structure. In this work, we propose a deep end-to-end recommendation framework named GSIRec to make full use of the graph side information. Specifically, GSIRec derives a multi-task learning approach that introduces a side information task to assist the recommendation task. The key idea is that we design a delicate knowledge assistance module to be the bridge between tasks, which captures useful knowledge to complement each task. Also, we utilize a graph attention method to exploit the topological structure of side information to enhance recommendation. To show the wide application and flexibility of our framework, we integrate side information from two aspects: social networks (for users) and knowledge graphs (for items). We apply GSIRec in two recommendation scenarios: social-aware recommendation and knowledge-aware recommendation. To evaluate the effectiveness of our framework, we conduct extensive experiments with four real-world public datasets. The results reveal that GSIRec consistently outperforms the state-of-the-art methods on the rating prediction task and top-K recommendation task. Moreover, GSIRec can alleviate data sparsity and cold start issues to some extent.