A Real-time Hybrid Clothing Recommender System: Integrating Content-Based Learning with User Interaction

A real-time hybrid recommender system for clothes is shown in this work. It uses a swipe-based interface in which users indicate their preferences by making simple gestures: left swipes for products they dislike and right swipes for items they like. This user-friendly interaction model improves usability and engagement by collecting user feedback with simplicity. In order to generate comprehensive item profiles, the system analyzes important garment qualities like color, pattern, material, design, and style using a combination of content-based and collaborative filtering algorithms. These profiles aid in the recommendation model’s efficient comprehension of user preferences. Concurrently, the model incorporates user interactions, creating an adaptive loop that continuously improves suggestions. The system is guaranteed to remain sensitive to changing preferences because to its dynamic feedback mechanism, which provides tailored suggestions that closely match each user’s preferred style. This hybrid strategy provides a scalable, responsive answer to issues like the cold start problem and low user engagement. Initial recommendations for new users are made possible by content features, and accuracy is gradually increased through ongoing learning from user interactions. By tailoring recommendations to each user’s preferences, this strategy not only increases user satisfaction and engagement but also increases conversion rates. The system also gives retailers useful information about consumer preferences and fashion trends, enabling them to make data-driven choices about marketing and inventories. All things considered, this recommender system is a potent instrument for the fashion sector, combining accuracy, versatility, and easy usability to provide incredibly customized experiences.