Social-semantic enhanced dual-intent hypergraph collaborative filtering

Abstract

Recommender systems provide personalized recommendations by modeling user–item interactions, where disentangling users’ intents is critical for improving recommendation accuracy. While existing intent modeling methods aim to capture fine-grained intent representations, they face two challenges: 1) Neglecting the influence of social semantics on modeling fine-grained intents; 2) Implicit data sparsity and intent redundancy limiting intent characterization. To tackle these challenges, we propose a Social-Semantic Enhanced Dual-Intent Hypergraph Collaborative Filtering (SDIHGCF) model. Specifically, SDIHGCF constructs hypergraph structures to preserve social semantics among users, items, and groups. It encodes features from both social and interest perspectives to achieve user and item representations that integrate individual intent, which signifies private preferences, and collective intent, which denotes overall awareness. To mitigate data sparsity and intent redundancy, where one intent can be represented by others, we use graph contrastive regularization to enforce consistency among users, items, intents, and interactions. Additionally, a bidirectional contrastive learning loss is proposed to enhance intent alignment. Experiments on four datasets demonstrate that SDIHGCF outperforms existing methods, offering novel insights into fine-grained intent modeling.