HGCRec: A Heterogeneous Graph Contrastive Learning Framework for Cold-Start Multi-Modal Recommendation in Intelligent Information Systems
TL;DR
Heterogeneous Graph Contrastive Recommendation is proposed, a heterogeneous graph contrastive learning framework for cold-start multi-modal recommendation that consistently outperforms representative graph-based and multi-modal recommendation baselines.
Abstract
Personalized recommendation has become an essential component of intelligent information systems and electronic multimedia platforms. However, cold-start items with limited user–item interactions remain difficult to model, especially when collaborative signals are sparse and heterogeneous side information is underutilized. To address this problem, this paper proposes Heterogeneous Graph Contrastive Recommendation (HGCRec), a heterogeneous graph contrastive learning framework for cold-start multi-modal recommendation. HGCRec constructs a heterogeneous structural graph containing users, items, categories, brands, and semantic entities, while visual and textual information is incorporated as item-side feature views. A relation-aware heterogeneous graph encoder captures relation-specific structural semantics, and a cross-modal graph contrastive learning objective coordinates structural, visual, and textual item representations. An interaction-sparsity-aware weighting strategy allocates stronger contrastive supervision to items with fewer training interactions. Furthermore, an item-specific adaptive fusion module integrates collaborative, structural, visual, and textual representations according to interaction sparsity and learned multi-source representation states. Experiments are conducted on three Amazon multi-modal recommendation datasets, including Baby, Sports, and Clothing. The results show that HGCRec consistently outperforms representative graph-based and multi-modal recommendation baselines. Compared with the strongest baseline, Freezing and Denoising Graph Structures for Multimodal Recommendation (FREEDOM), HGCRec improves Recall@20 by 9.65%, 10.44%, and 12.00% on Baby, Sports, and Clothing, respectively, and improves NDCG@20 by 11.14%, 11.35%, and 13.10%. Sparsity-aware analysis further shows larger relative improvements for items with extremely limited training interactions, demonstrating the effectiveness of HGCRec under the evaluated interaction-sparse and few-shot settings.