ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation

2026-04-15

AI Summary

This work addresses the challenge of effectively integrating ID-based and graph-based views to enhance user and item representations when only interaction data are available. To this end, we propose MVCrec, a novel framework that, for the first time, introduces multi-view contrastive learning between ID and graph views without relying on auxiliary information. MVCrec employs a triple contrastive objective encompassing intra-sequence, intra-graph, and cross-view contrasts, along with a multi-view fusion module that combines global and local attention mechanisms to jointly optimize user and item embeddings. Extensive experiments on five real-world datasets demonstrate that MVCrec significantly outperforms eleven state-of-the-art baselines, achieving relative improvements of up to 14.44% in NDCG@10 and 9.22% in HitRatio@10.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Recommender Systems
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historical interaction sequences and predict items a user is likely to engage with next. Recent advances have leveraged contrastive learning and graph neural networks to learn more expressive representations from interaction histories -- graphs capture relational structure between nodes, while ID-based representations encode item-specific information. However, few studies have explored multi-view contrastive learning between ID and graph perspectives to jointly improve user and item representations, especially in settings where only interaction data is available without auxiliary information. To address this gap, we propose Multi-View Contrastive learning for sequential recommendation (MVCrec), a framework that integrates complementary signals from both sequential (ID-based) and graph-based views. MVCrec incorporates three contrastive objectives: within the sequential view, within the graph view, and across views. To effectively fuse the learned representations, we introduce a multi-view attention fusion module that combines global and local attention mechanisms to estimate the likelihood of a target user purchasing a target item. Comprehensive experiments on five real-world benchmark datasets demonstrate that MVCrec consistently outperforms 11 state-of-the-art baselines, achieving improvements of up to 14.44\% in NDCG@10 and 9.22\% in HitRatio@10 over the strongest baseline. Our code and datasets are available at https://github.com/sword-Lz/MMCrec.

Problem

• sequential recommendation
• contrastive learning
• graph neural networks
• multi-view learning
• representation learning

Innovation

• multi-view contrastive learning
• sequential recommendation
• graph neural networks
• attention fusion
• ID-based representation