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.
@article{arxiv.2504.15085,
title = {Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation},
author = {Wangyu Wu and Zhenhong Chen and Siqi Song and Xianglin Qiu and Xiaowei Huang and Fei Ma and Jimin Xiao},
journal= {arXiv preprint arXiv:2504.15085},
year = {2025}
}
Comments
Accepted at CogSCI 2025. arXiv admin note: text overlap with arXiv:2502.15694