Personalized Fashion Recommendations for Diverse Body Shapes and Local Preferences with Contrastive Multimodal Cross-Attention Network

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jianghong Ma, Huiyue Sun, Dezhao Yang, Haijun Zhang
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引用次数: 0

Abstract

Fashion recommendation has become a prominent focus in the realm of online shopping, with various tasks being explored to enhance the customer experience. Recent research has particularly emphasized fashion recommendation based on body shapes, yet a critical aspect of incorporating multimodal data relevance has been overlooked. In this paper, we present the Contrastive Multimodal Cross-Attention Network, a novel approach specifically designed for fashion recommendation catering to diverse body shapes. By incorporating multimodal representation learning and leveraging contrastive learning techniques, our method effectively captures both inter- and intra-sample relationships, resulting in improved accuracy in fashion recommendations tailored to individual body types. Additionally, we propose a locality-aware cross-attention module to align and understand the local preferences between body shapes and clothing items, thus enhancing the matching process. Experimental results conducted on a diverse dataset demonstrate the state-of-the-art performance achieved by our approach, reinforcing its potential to significantly enhance the personalized online shopping experience for consumers with varying body shapes and preferences.

利用对比多模态交叉注意力网络,针对不同体型和地方偏好提供个性化时尚推荐
时尚推荐已成为在线购物领域的一个突出焦点,人们正在探索各种任务来提升客户体验。最近的研究特别强调基于体型的时尚推荐,但却忽略了结合多模态数据相关性的一个重要方面。在本文中,我们介绍了对比多模态交叉注意力网络,这是一种新颖的方法,专门用于针对不同体形的时尚推荐。通过结合多模态表征学习和利用对比学习技术,我们的方法有效地捕捉了样本间和样本内的关系,从而提高了针对不同体型的时尚推荐的准确性。此外,我们还提出了一个局部感知交叉关注模块,以调整和理解体型与服装之间的局部偏好,从而增强匹配过程。在一个多样化数据集上进行的实验结果表明,我们的方法达到了最先进的性能,增强了其为具有不同体型和偏好的消费者显著提升个性化在线购物体验的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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