S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

TL;DR

S^3-Rec introduces a self-supervised learning approach using mutual information maximization to enhance sequential recommendation models, especially under data sparsity, by capturing intrinsic data correlations through auxiliary tasks.

Contribution

The paper proposes S^3-Rec, a novel self-supervised framework that leverages mutual information maximization to improve sequential recommendation performance.

Findings

Outperforms state-of-the-art methods on six real-world datasets.

Effective especially with limited training data.

Extends to improve other recommendation models.

Abstract

Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation. To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture. The main idea of our approach is to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence by utilizing the mutual information maximization (MIM) principle. MIM provides a unified way to characterize the correlation between different types of data, which is particularly suitable in our scenario. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available. Besides, we extend our self-supervised learning method to other recommendation models, which also improve their performance.