用图像信息提升跨域推荐效果，让系统更懂用户视觉偏好。
Image Fusion for Cross-Domain Sequential Recommendation
- 引入冻结的CLIP模型生成图像嵌入，融合视觉特征增强物品表示。
- 通过多注意力机制捕捉跨域兴趣，实现单域与跨域偏好联合学习。
- 在四个电商数据集上验证，显著优于现有方法，适合视觉推荐场景。
跨域序列推荐（CDSR）旨在基于多领域历史交互预测用户未来行为。其核心挑战在于充分挖掘跨域用户偏好，有效利用物品在序列内与序列间的交互信息。本文提出一种新方法——图像融合跨域序列推荐（IFCDSR），通过引入物品图像信息来更好捕捉视觉偏好。该方法采用冻结的CLIP模型生成图像嵌入，将视觉数据融入序列内与序列间交互的原始物品表示中。同时，设计多注意力层以捕获跨域兴趣，实现单域与跨域用户偏好的联合学习。为验证IFCDSR的有效性，我们在四个电商数据集上重新划分并进行大量实验。结果表明，IFCDSR显著优于现有方法。
原文摘要 · Abstract (English)
Cross-Domain Sequential Recommendation (CDSR) aims to predict future user interactions based on historical interactions across multiple domains. The key challenge in CDSR is effectively capturing cross-domain user preferences by fully leveraging both intra-sequence and inter-sequence item interactions. In this paper, we propose a novel method, Image Fusion for Cross-Domain Sequential Recommendation (IFCDSR), which incorporates item image information to better capture visual preferences. Our approach integrates a frozen CLIP model to generate image embeddings, enriching original item embeddings with visual data from both intra-sequence and inter-sequence interactions. Additionally, we employ a multiple attention layer to capture cross-domain interests, enabling joint learning of single-domain and cross-domain user preferences. To validate the effectiveness of IFCDSR, we re-partitioned four e-commerce datasets and conducted extensive experiments. Results demonstrate that IFCDSR significantly outperforms existing methods.
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