Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914014828691456 |
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| author | Wu, Wangyu Chen, Zhenhong Song, Siqi Qiu, Xianglin Huang, Xiaowei Ma, Fei Xiao, Jimin |
| author_facet | Wu, Wangyu Chen, Zhenhong Song, Siqi Qiu, Xianglin Huang, Xiaowei Ma, Fei Xiao, Jimin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation Wu, Wangyu Chen, Zhenhong Song, Siqi Qiu, Xianglin Huang, Xiaowei Ma, Fei Xiao, Jimin Computer Vision and Pattern Recognition 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. |
| title | Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.15085 |