Hierarchical Attention Fusion of Visual and Textual Representations for Cross-Domain Sequential Recommendation

• URL: http://arxiv.org/abs/2504.15085v1
• Date: Mon, 21 Apr 2025 13:18:54 GMT
• Title: Hierarchical Attention Fusion of Visual and Textual Representations for Cross-Domain Sequential Recommendation
• Authors: Wangyu Wu, Zhenhong Chen, Siqi Song, Xianglin Qiua, Xiaowei Huang, Fei Ma, Jimin Xiao,
• Abstract summary: Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains.<n>We propose Hierarchical Attention Fusion of Visual and Textual Representations (HAF-VT), a novel approach integrating visual and textual data to enhance cognitive modeling.<n>A hierarchical attention mechanism jointly learns single-domain and cross-domain preferences, mimicking human information integration.
• Score: 19.654959889052638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences through intra- and inter-sequence item relationships. Inspired by human cognitive processes, we propose Hierarchical Attention Fusion of Visual and Textual Representations (HAF-VT), a novel approach integrating visual and textual data to enhance cognitive modeling. Using the frozen CLIP model, we generate image and text embeddings, enriching item representations with multimodal data. A hierarchical attention mechanism jointly learns single-domain and cross-domain preferences, mimicking human information integration. Evaluated on four e-commerce datasets, HAF-VT outperforms existing methods in capturing cross-domain user interests, bridging cognitive principles with computational models and highlighting the role of multimodal data in sequential decision-making.

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