BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
<|reference_start|>BiVRec: Bidirectional View-based Multimodal Sequential Recommendation: The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential recommendation models, the mainstream paradigm was ID-dominant recommendations, wherein multimodal information was fused as side information. However, due to their limitations in terms of transferability and information intrusion, another paradigm emerged, wherein multimodal features were employed directly for recommendation, enabling recommendation across datasets. Nonetheless, it overlooked user ID information, resulting in low information utilization and high training costs. To this end, we propose an innovative framework, BivRec, that jointly trains the recommendation tasks in both ID and multimodal views, leveraging their synergistic relationship to enhance recommendation performance bidirectionally. To tackle the information heterogeneity issue, we first construct structured user interest representations and then learn the synergistic relationship between them. Specifically, BivRec comprises three modules: Multi-scale Interest Embedding, comprehensively modeling user interests by expanding user interaction sequences with multi-scale patching; Intra-View Interest Decomposition, constructing highly structured interest representations using carefully designed Gaussian attention and Cluster attention; and Cross-View Interest Learning, learning the synergistic relationship between the two recommendation views through coarse-grained overall semantic similarity and fine-grained interest allocation similarity BiVRec achieves state-of-the-art performance on five datasets and showcases various practical advantages.<|reference_end|>
@article{hu2024bivrec:,
    title={BiVRec: Bidirectional View-based Multimodal Sequential Recommendation},
    author={Jiaxi Hu, Jingtong Gao, Xiangyu Zhao, Yuehong Hu, Yuxuan Liang, Yiqi
  Wang, Ming He, Zitao Liu, Hongzhi Yin},
    journal={arXiv preprint arXiv:2402.17334},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17334},
    primaryClass={cs.IR cs.AI}
}