BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要