Inhomogeneous Interest Modeling via Hypergraph Convolutional Networks for Social Recommendation

To improve the performance of social recommendation, it can be beneficial to include social relationships, model user interests, and item attraction. Traditional methods use binary social relationships between users and items, known as ’user-user’ and ’user-item’, which rely on historical interactions like purchasing history and clickiing history equally. However, the interests and attractions aggregated by such approaches are incomplete. In addition to binary interactions, there are also a large number of ’user-item-user’ triple social behaviors, such as retweeting or recommending. Considering these triple social behaviors introduces inhomogeneity to the modeling of interests and attractions. Furthermore, beyond the primary explicit in-terest derived from interaction history, users are also influenced by secondary, latent interests in items shared by friends, which makes the modeling more complex. To address this issue, we propose IIMHGCN (Inhomogeneous Interest Modeling via Hy-pergraph Convolutional Networks). In addition to binary social relationships, we incorporate a hypergraph model that utilizes triple social recommendation behaviors and higher-order social connections to model inhomogeneous interests and attraction. We substantiate its effectiveness through extensive experiments on two real-world datasets.