Trendy fashion recommender system by machine learning (KEY IJP************923)

• Danish Khan,Rishabh Jain

Abstract

Fashion recommendation systems, powered by ML (machine learning) algorithms, offer customized clothing,with accessory suggestions based on user choices, past actions, furthermore current fashion. This researchpaper focuses centrally on collaborative filtering and content-based filtering techniques as it extensivelyinvestigates the development of these systems. Hybrid models are additionally explored; these modelsthoroughly combine the described approaches to produce elevated accuracy and increased user satisfaction.The results from all of these implementations show that the recommendations experienced largeimprovement, greatly increasing both user engagement and purchasing rates.A recommendation program or algorithm extensively examines many datasets to identify and presentapplicable information, thereby giving all customers customized suggestions. These systems look at patternsand trends related to users and what they choose, so businesses can better understand what customers do andlike.Fashion recommender systems are very important in online retail because they show stores, using detailedanalysis, which products to offer based on what customers like, view, and buy. They can predict the demandfor all specific clothing items, guaranteeing that stores have enough popular products and avoid too manyunwanted items. Sales improve when customers are shown products they may enjoy, and the chances ofselling more increase. With technical improvements, these systems will continue to improve at providingrecommendations that are more personalized and accurate. A few companies, such as Amazon, Flipkart,Myntra, Ajio, as well as TATA Cliq, use recommender systems in order to both simplify product offeringsalong with improving customer experience.