Image Fusion for Cross-Domain Sequential Recommendation

• URL: http://arxiv.org/abs/2502.15694v2
• Date: Thu, 27 Feb 2025 03:08:16 GMT
• Title: Image Fusion for Cross-Domain Sequential Recommendation
• Authors: Wangyu Wu, Siqi Song, Xianglin Qiu, Xiaowei Huang, Fei Ma, Jimin Xiao,
• Abstract summary: Cross-Domain Sequential Recommendation aims to predict future user interactions based on historical interactions across multiple domains.<n>Key challenge in CDSR is effectively capturing cross-domain user preferences by fully leveraging both intra-sequence and inter-sequence item interactions.<n>We propose a novel method, Image Fusion for Cross-Domain Sequential Recommendation (IFCDSR), which incorporates item image information to better capture visual preferences.
• Score: 20.37668418178215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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