Integrating User-Group relationships under interest similarity constraints for social recommendation

摘要

Traditional collaborative filtering based recommender systems generally suffer from the interaction data sparsity problem. Therefore, social recommendation is proposed to mitigate the issue and improve recommendation performance by introducing social information. Existing social recommendation studies primarily focus on the direct connections between users, such as friendship or users’ correlation. Unfortunately, there often is a severe data sparsity issue in above social data as well, which limits the performance of these models. In contrast, user-group relationships, another valuable social information that is formed by users joining the groups they are interested in, have received insufficient attention. In this paper, we focus on this relationship, demonstrate its excellent effectiveness in alleviating the problem of data sparsity, and integrate it into our recommendation model IGRec (Integrating user-Group relationships for social Recommendation) in a reasonable way. Specifically, to address the problem that existing group-information-enhanced methods have not modeled users’ collaborative interests and social influence in depth, we reformulate the available data into two bipartite graphs: user-item graph and user-group graph. And then employ more robust high-order GCN-based model combining a multi-layer attention mechanism to learn user and item representation from two graphs. Furthermore, we notice that due to the high complexity of user-group networks, the interests of some users in the same group may be far different, especially in those large-scale groups. The indiscriminate use of high-order neighbors’ information in user-group graph may result in the introduction of negative information during the embedding propagation. Thus, to obtain a more precise representation for user and item, we propose to constrain the graph convolution operations at the social side inside subgraphs composed of users with similar interests and the groups they have joined in our model. Finally, experimental results on three real-world datasets clearly show the effectiveness of our proposed model.