用大模型增强图文融合，提升跨域序列推荐效果
LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation
- 用冻结的CLIP模型提取图文嵌入，融合多模态信息增强物品表征
- 多注意力机制联合学习单域与跨域偏好，捕捉复杂用户兴趣
- 在4个电商数据集上超越现有方法，适合做跨域推荐的研究者
跨域序列推荐（CDSR）通过利用多个领域的历史交互行为预测用户行为，重点在于建模跨域偏好并捕捉物品间的内在及跨序列关系。本文提出一种名为LLM-EMF的新方法，通过引入大语言模型知识增强文本信息，并结合视觉与文本数据实现高效融合，显著提升推荐性能。采用冻结的CLIP模型生成图像和文本嵌入，从而丰富物品表征。多注意力机制联合学习单域与跨域偏好，有效捕捉和理解多样化领域中的复杂用户兴趣。在四个电商平台数据集上的评估表明，LLM-EMF在建模跨域用户偏好方面持续优于现有方法，验证了多模态数据融合的有效性及其在序列推荐系统中的优势。代码将开源。
原文摘要 · Abstract (English)
Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences and capturing both intra- and inter-sequence item relationships. We propose LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation (LLM-EMF), a novel and advanced approach that enhances textual information with Large Language Models (LLM) knowledge and significantly improves recommendation performance through the fusion of visual and textual data. Using the frozen CLIP model, we generate image and text embeddings, thereby enriching item representations with multimodal data. A multiple attention mechanism jointly learns both single-domain and cross-domain preferences, effectively capturing and understanding complex user interests across diverse domains. Evaluations conducted on four e-commerce datasets demonstrate that LLM-EMF consistently outperforms existing methods in modeling cross-domain user preferences, thereby highlighting the effectiveness of multimodal data integration and its advantages in enhancing sequential recommendation systems. Our source code will be released.
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