Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation

• URL: http://arxiv.org/abs/2501.14269v2
• Date: Thu, 30 Jan 2025 02:05:07 GMT
• Title: Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
• Authors: Shengzhe Zhang, Liyi Chen, Dazhong Shen, Chao Wang, Hui Xiong,
• Abstract summary: We propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) First MoE, named Interactive MoE, extracts essential user interest-related information from the multi-modal data of each item. Second MoE, termed Temporal MoE, captures user dynamic interests by introducing explicit temporal embeddings from timestamps in modality encoding.
• Score: 19.47124940518026
• License:
• Abstract: Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and industry. Existing methods typically focus on enhancing multi-modal information utility through adaptive modality fusion to capture the evolving of user preference from user-item interaction sequences. However, most of them overlook the interference caused by redundant interest-irrelevant information contained in rich multi-modal data. Additionally, they primarily rely on implicit temporal information based solely on chronological ordering, neglecting explicit temporal signals that could more effectively represent dynamic user interest over time. To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy. Specifically, the first MoE, named Interactive MoE, extracts essential user interest-related information from the multi-modal data of each item. Then, the second MoE, termed Temporal MoE, captures user dynamic interests by introducing explicit temporal embeddings from timestamps in modality encoding. To further address data sparsity, we propose three auxiliary supervision tasks: sequence-level category prediction (CP) for item feature understanding, contrastive learning on ID (IDCL) to align sequence context with user interests, and placeholder contrastive learning (PCL) to integrate temporal information with modalities for dynamic interest modeling. Extensive experiments on four public datasets verify the effectiveness of HM4SR compared to several state-of-the-art approaches.

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