Semantic Augmented Multiview Graph Learning With Frequency-Aware Propagation for Recommendation

Abstract

Graph neural networks (GNNs) have been widely adopted in collaborative filtering to model higher order relationships between users and items. However, existing methods mainly focus on interaction-based structures and neglect the explicit modeling of semantic relations among users and among items. Within these graph structures, deep graph message propagation tends to cause over-smoothing, where node embeddings converge to similar representations, hindering effective personalization. To address these challenges, we propose a semantic-augmented multiview graph-based recommendation framework (SAGRec). Specifically, SAGRec constructs semantic similarity graphs based on embeddings generated by large language models to capture semantic relations between users and between items. Next, we propose a frequency-aware propagation mechanism. It leverages representation differences between nodes and incorporates gated message passing to retain node distinctions during propagation. To further mitigate representation drift induced by multilayer propagation, we design an adaptive inter-layer contrastive learning strategy that enforces consistency while maintaining discriminability across layers. Comprehensive empirical evaluations conducted on three real-world datasets show that SAGRec achieves consistently superior performance over baseline methods with respect to normalized discounted cumulative gain and recall.