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.

翻译：序列推荐在学术界和工业界日益凸显其重要性，尤其在电子商务领域。其主要目标是从历史交互序列中提取用户偏好，并预测用户下一个可能交互的物品。近期进展利用对比学习和图神经网络，从交互历史中学习更具表现力的表示——图捕捉节点间的关系结构，而基于ID的表示则编码物品特定信息。然而，很少有研究探索ID视角与图视角之间的多视图对比学习，以联合改进用户和物品表示，尤其是在仅有交互数据而无辅助信息的场景下。为填补这一空白，我们提出用于序列推荐的多视图对比学习框架（MVCrec），该框架整合了来自序列（基于ID）和图视角的互补信号。MVCrec包含三个对比目标：序列视图内、图视图内以及跨视图。为有效融合学习到的表示，我们引入多视图注意力融合模块，结合全局与局部注意力机制，以估计目标用户购买目标物品的可能性。在五个真实世界基准数据集上的全面实验表明，MVCrec持续优于11个最先进基线，在最强基线上，NDCG@10提升高达14.44%，HitRatio@10提升9.22%。我们的代码和数据集可在https://github.com/sword-Lz/MMCrec获取。