Semantic Enhanced Heterogeneous Hypergraph Network for Collaborative Filtering

Abstract

Collaborative Filtering (CF) based on graph neural networks (GNNs) has yielded immense success for recommendation systems by capturing high-order dependencies from implicit feedback. Recently, the outstanding text comprehension ability of the Large Language Models (LLMs) has shown promising potential to provide auxiliary semantics for collaborative representation. However, when aligning textual information with collaborative signals, inconsistent semantics between user-item and item-item text pairs may lead to the degradation of the alignment model, thus hindering the recommender system from effectively utilizing heterogeneous information. In this paper, we propose a novel method: Semantic Enhanced Heterogeneous Hypergraph Network (SEHHN), which enhances the representations of CF correlations with semantics, thereby avoiding alignment degradation. To better model the collaborative signals, we design a graph autoencoder that captures the bidirectional relationship between user preferences and item features in review semantics. Furthermore, we develop an LLM-based item classifier to adaptively exploit potential correlations of items via the co-occurrences of item features. Finally, we design a heterogeneous hypergraph network to achieve efficient alignment and propagation of heterogeneous information, thereby alleviating the impact of semantic inconsistency on CFs. Extensive experiments on three real-world datasets demonstrate that our proposed SEHHN outperforms existing SOTA methods and validates the effectiveness of each component.