Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation

TL;DR

This paper introduces a hierarchical preference modeling framework using dual-transformers and contrastive learning to improve sequential recommendation accuracy by capturing both low- and high-level user preferences.

Contribution

The paper proposes a novel dual-transformer and contrastive learning scheme for modeling complex user preferences at multiple levels in sequential recommendation systems.

Findings

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

Effectively captures both low- and high-level user preferences.

Enhances recommendation accuracy through semantics-aware context embedding.

Abstract

Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users' single low-level preference based on item ID information while ignoring the high-level preference revealed by item attribute information, such as item category. Furthermore, they often utilize limited sequence context information to predict the next item while overlooking richer inter-item semantic relations. To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation. Specifically, in the framework, a novel dual-transformer module and a novel dual contrastive learning scheme have been designed to discriminatively learn users' low- and high-level preference and to effectively enhance both low- and high-level preference learning respectively. In addition, a novel semantics-enhanced context embedding module has been devised to generate more informative context embedding for further improving the recommendation performance. Extensive experiments on six real-world datasets have demonstrated both the superiority of our proposed method over the state-of-the-art ones and the rationality of our design.