KiseKloset for Fashion Retrieval and Recommendation

[2506.23471] KiseKloset for Fashion Retrieval and Recommendation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Information Retrieval arXiv:2506.23471 (cs) [Submitted on 30 Jun 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: KiseKloset for Fashion Retrieval and Recommendation Authors: Thanh-Tung Phan-Nguyen , Khoi-Nguyen Nguyen-Ngoc , Tam V. Nguyen , Minh-Triet Tran , Trung-Nghia Le View a PDF of the paper titled KiseKloset for Fashion Retrieval and Recommendation, by Thanh-Tung Phan-Nguyen and Khoi-Nguyen Nguyen-Ngoc and Tam V. Nguyen and Minh-Triet Tran and Trung-Nghia Le View PDF HTML (experimental) Abstract: The global fashion e-commerce industry has become integral to people's daily lives, leveraging technological advancements to offer personalized shopping experiences, primarily through recommendation systems that enhance customer engagement through personalized suggestions. To improve customers' experience in online shopping, we propose a novel comprehensive KiseKloset system for outfit retrieval and recommendation. We explore two approaches for outfit retrieval: similar item retrieval and text feedback-guided item retrieval. Notably, we introduce a novel transformer architecture designed to recommend complementary items from diverse categories. Furthermore, we enhance the overall performance of the search pipeline by integrating approximate algorithms to optimize the search process. Additionally, addressing the crucial needs of online shoppers, we employ a lightweight yet efficient virtual try-on framework capable of real-time operation, memory efficiency, and maintaining realistic outputs compared to its predecessors. This virtual try-on module empowers users to visualize specific garments on themselves, enhancing the customers' experience and reducing costs associated with damaged items for retailers. We deployed our end-to-end system for online users to test and provide feedback, enabling us to measure their satisfaction levels. The results of our user study revealed that 84% of participants found our comprehensive system highly useful, significantly improving their online shopping experience. Subjects: Information Retrieval (cs.IR) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2506.23471 [cs.IR] (or arXiv:2506.23471v2 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2506.23471 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Trung Nghia Le [ view email ] [v1] Mon, 30 Jun 2025 02:25:39 UTC (11,312 KB) [v2] Sat, 11 Apr 2026 18:08:18 UTC (9,982 KB) Full-text links: Access Paper: View a PDF of the paper titled KiseKloset for Fashion Retrieval and Recommendation, by Thanh-Tung Phan-Nguyen and Khoi-Nguyen Nguyen-Ngoc and Tam V. Nguyen and Minh-Triet Tran and Trung-Nghia Le View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2025-06 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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