融合图文特征与用户行为序列，显著提升推荐精准度
Multimodal Enhancement of Sequential Recommendation
- 构建物品间图文相似图，结合协同过滤信号
- 短长期偏好捕捉使性能最高提升33.5%
- 小数据集上用户嵌入可使短期指标提升200%
我们提出一种新型推荐框架MuSTRec（基于多模态与序列的Transformer推荐），统一多模态与序列推荐范式。通过提取文本和视觉特征构建物品-物品图，捕捉跨物品相似性与协同过滤信号；引入基于频率的自注意力模块，有效建模用户的短期与长期偏好。在多个Amazon数据集上，MuSTRec相比现有最先进方法性能提升高达33.5%。此外，我们揭示了该范式的新需求：需采用新的数据划分策略，并发现将用户嵌入融入序列推荐可在较小数据集上使短期指标提升达200%。代码已公开于https://anonymous.4open.science/r/MuSTRec-D32B/，后续将正式发布。
原文摘要 · Abstract (English)
We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuSTRec captures cross-item similarities and collaborative filtering signals, by building item-item graphs from extracted text and visual features. A frequency-based self-attention module additionally captures the short- and long-term user preferences. Across multiple Amazon datasets, MuSTRec demonstrates superior performance (up to 33.5% improvement) over multimodal and sequential state-of-the-art baselines. Finally, we detail some interesting facets of this new recommendation paradigm. These include the need for a new data partitioning regime, and a demonstration of how integrating user embeddings into sequential recommendation leads to drastically increased short-term metrics (up to 200% improvement) on smaller datasets. Our code is availabe at https://anonymous.4open.science/r/MuSTRec-D32B/ and will be made publicly available.
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