Disentangled Modeling of Social Homophily and Influence for Social Recommendation
Nian Li, Chen Gao, Depeng Jin, Qingmin Liao · IEEE Transactions on Knowledge and Data Engineering · 2022
Social recommendation leverages social information to alleviate data sparsity and cold-start issues of collaborative filtering (CF) methods. Most existing works model user interests following the assumption ofsocial homophilybased on social-relation data. The explicit modeling ofsocial influence, which also largely affects user behaviors, has not been well explored. Considering user behaviors may be driven by social factors in today’s information services (e.g., purchasing products shared by close friends on social e-commerce applications), these methods will be suboptimal. In this work, we propose a method modeling both social homophily-aware user interests and social influence as two essential effects on user behaviors for social recommendation, named as DISGCN (short forDISentangled modeling of Social homophily and influence withGraphConvolutionalNetwork). Specifically, we devise a disentangled embedding layer to encode these two effects. Furthermore, two tailored graph convolutional layers are developed to disentangle them refinedly, leveraging the high-order embedding propagation in social-network graph from two aspects. Technically, first, the operation of attentive embedding propagation is adopted for capturing personalized social homophily-aware interests, and second, the item-gate-based embedding propagation is proposed for capturing item-specific social influence. In addition, to ensure the disentanglement of social influence, we propose a contrastive learning framework that endows corresponding embeddings with explicit semantics. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model. Further studies also verify the rationality and necessity of our designs. We have released the datasets and codes at this link:https://github.com/tsinghua-fib-lab/DISGCN.