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arXiv · 2502.15694

Image Fusion for Cross-Domain Sequential Recommendation

Wangyu Wu·Siqi Song·Xianglin Qiu·Xiaowei Huang·Fei Ma·Jimin Xiao
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Abstract

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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Wangyu Wu, Siqi Song, Xianglin Qiu, Xiaowei Huang, Fei Ma, Jimin Xiao. 2024-12-31. Image Fusion for Cross-Domain Sequential Recommendation. https://arxiv.org/abs/2502.15694

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