Contrastive Learning for Sequential Recommendation

TL;DR

This paper introduces CL4SRec, a contrastive learning-based model for sequential recommendation that enhances user representations by combining traditional prediction with self-supervised learning, leading to improved performance.

Contribution

It proposes a novel multi-task framework integrating contrastive learning with sequential recommendation, along with three data augmentation methods, to better capture user preferences.

Findings

Achieves state-of-the-art results on four public datasets.

Effectively mitigates data sparsity issues in user modeling.

Demonstrates the benefit of contrastive learning in sequential recommendation.

Abstract

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions. Despite their success, we argue that these approaches usually rely on the sequential prediction task to optimize the huge amounts of parameters. They usually suffer from the data sparsity problem, which makes it difficult for them to learn high-quality user representations. To tackle that, inspired by recent advances of contrastive learning techniques in the computer version, we propose a novel multi-task model called \textbf{C}ontrastive \textbf{L}earning for \textbf{S}equential \textbf{Rec}ommendation~(\textbf{CL4SRec}). CL4SRec not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences. Therefore, it can extract more meaningful user patterns and further encode the user representation effectively. In addition, we propose three data augmentation approaches to construct self-supervision signals. Extensive experiments on four public datasets demonstrate that CL4SRec achieves state-of-the-art performance over existing baselines by inferring better user representations.