AI-Powered Fashion Recommendation System for Smart Dress Rental Platforms

This paper proposes a smart recommendation system for online dress rental platforms, leveraging Artificial Intelligence (AI) to enhance user personalization. It addresses the challenge user’s face in selecting attire by providing intelligent, data-driven suggestions tailored to their preferences, body profile, and booking history. Using machine learning techniques such as collaborative and content-based filtering, the system suggests dresses that match user taste, improving the platform’s usability, engagement, and conversion rate. The solution integrates seamlessly with existing web applications built using Java, JSP, and MySQL, and includes a micro service-based architecture for AI modules using Python. The outcome shows measurable improvements in user interaction and rental frequency.

Authors

Publication Details

Journal
Indian Journal of Computer Science and Technology
Published
2025-06-02
DOI
https://doi.org/10.59256/indjcst.20250402029
Primary Topic
Consumer Perception and Purchasing Behavior
Type
article
Field-Weighted Citation Impact
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article

AI-Powered Fashion Recommendation System for Smart Dress Rental Platforms

Bhagyashree Kumbhare, Yamini B. Laxane, Pratiksha A. Chambhare, Monali N. Ninawe
Indian Journal of Computer Science and Technology
Consumer Perception and Purchasing Behavior
article

AI-Powered Fashion Recommendation System for Smart Dress Rental Platforms

Bhagyashree Kumbhare, Yamini B. Laxane, Pratiksha A. Chambhare, Monali N. Ninawe
article en

Abstract

This paper proposes a smart recommendation system for online dress rental platforms, leveraging Artificial Intelligence (AI) to enhance user personalization. It addresses the challenge user’s face in selecting attire by providing intelligent, data-driven suggestions tailored to their preferences, body profile, and booking history. Using machine learning techniques such as collaborative and content-based filtering, the system suggests dresses that match user taste, improving the platform’s usability, engagement, and conversion rate. The solution integrates seamlessly with existing web applications built using Java, JSP, and MySQL, and includes a micro service-based architecture for AI modules using Python. The outcome shows measurable improvements in user interaction and rental frequency.

Indian Journal of Computer Science and Technology
Openalex Percentile: Top 85%
Consumer Perception and Purchasing Behavior
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