The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.

该文提出了一种新的推荐框架，名为序列推荐的图对比学习（GCL4SR），它使用一种称为加权项目转换图 （WITG）的模型，建立在所有用户的交互序列上，通过使用WITG子图来增强每个交互序列的表示和最大化同一交互序列引导的增强表示之间的一致性，最小化WITG全局上下文引导的增强表示和原始序列本地表示之间的差异，并在真实世界数据集上展示显着优于现有方法的性能。

利用图形对比学习增强序列推荐