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arXiv · 2410.22790

Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation

Chengkai Huang·Shoujin Wang·Xianzhi Wang·Lina Yao
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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.

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Chengkai Huang, Shoujin Wang, Xianzhi Wang, Lina Yao. 2024-10-30. Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation. https://arxiv.org/abs/2410.22790

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