Overlapping Community-aware Social Recommendation via Graph Attention Network Method

Social influence plays a crucial role in shaping user preferences and behaviors, making social recommendation an effective approach for alleviating the cold-start problem. However, most existing social recommendation methods either model social influence at the individual level or assume non-overlapping community structures, which fails to reflect the fact that users typically belong to multiple communities simultaneously. As a result, the influence from different communities and their cross-community interactions are not explicitly modeled. In this paper, we formally study the problem of overlapping community-aware social recommendation, where a user's preference is jointly influenced by personal behavior, social neighbors, and multiple overlapping communities, each contributing differently depending on the target item. To address this problem, we propose GANOC, a unified framework that decomposes user preference into three complementary domains: personal, social, and community. We employ graph attention networks to model influence propagation in both social and community graphs, and design an item-aware attention mechanism to selectively aggregate cross-community influences. Furthermore, a domain attention network is introduced to adaptively integrate preference representations from different domains for rating prediction. Extensive experiments on three real-world benchmark datasets demonstrate that GANOC outperforms state-of-the-art social recommendation methods, particularly for cold-start users, validating the effectiveness of explicitly modeling overlapping community influence.