KiseKloset for Fashion Retrieval and Recommendation

TL;DR

This paper introduces KiseKloset, a comprehensive fashion retrieval and recommendation system with novel transformer-based outfit suggestions, real-time virtual try-on, and optimized search algorithms, significantly enhancing online shopping experiences.

Contribution

It presents a new integrated system combining outfit retrieval, recommendation, virtual try-on, and optimized search, with user feedback demonstrating high satisfaction.

Findings

84% of users found the system highly useful

Introduced a transformer architecture for recommending complementary items

Achieved real-time, memory-efficient virtual try-on performance

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.