Fashion Recommendation System

Abstract

Fashion recommendation systems have become increasingly essential in the e-commerce industry, providing personalized outfit suggestions to users, enhancing their shopping experience, and boosting sales. This paper presents a novel approach to fashion recommendation by combining machine learning and deep learning techniques. We leverage a comprehensive dataset of user preferences and fashion items to create a robust recommendation system. Our approach first employs collaborative filtering and matrix factorization methods to establish user-item interactions. Subsequently, deep learning models, such as neural collaborative filtering and recurrent neural networks, are utilized to capture intricate patterns within the fashion data. This combination enables the system to offer personalized fashion recommendations based on the user's historical choices, style, and real-time Behaviour. The evaluation of our system demonstrates its effectiveness in enhancing user engagement and satisfaction while increasing the platform's revenue. The proposed fashion recommendation system showcases the potential of integrating machine learning and deep learning for optimizing personalized fashion suggestions in the ever- evolving fashion e-commerce landscape. This research contributes to the broader field of recommendation systems and their applications in the fashion industry.