Enhancing Sequential Recommendation with Graph Contrastive Learning

TL;DR

This paper introduces GCL4SR, a novel framework that enhances sequential recommendation by leveraging graph contrastive learning and global context from a weighted item transition graph to improve sequence representations.

Contribution

It proposes a new graph contrastive learning approach with global context integration and data augmentation for sequential recommendation systems.

Findings

GCL4SR outperforms existing methods on real-world datasets.

The use of global context reduces noise in sequence data.

Contrastive learning improves representation quality.

Abstract

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.