KiseKloset: Comprehensive System For Outfit Retrieval, Recommendation, And Try-On
This addresses the need for personalized and interactive shopping experiences in fashion e-commerce, though it appears incremental by integrating existing approaches with optimizations.
The authors tackled the problem of enhancing online fashion shopping by proposing KiseKloset, a comprehensive system for outfit retrieval, recommendation, and virtual try-on, with results showing 84% of participants found it highly useful.
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, recommendation, and try-on. 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.