AI-BASED OUTFIT RECOMMENDATION SYSTEM
Yaswanthraj S1, Mohithra S2, Pavithra Kumar Nadar3, Shakthi S4
1 Assistant professor, Department of Computer Science and Engineering (CYBER SECURITY) Sri Shakthi Institute of Engineering and Technology Coimbatore, India
2 2nd year student, Department of Computer Science and Engineering (CYBER SECURITY) Sri Shakthi Institute of Engineering and Technology Coimbatore, India
3 2nd year student, Department of Computer Science and Engineering (CYBER SECURITY) Sri Shakthi Institute of Engineering and Technology Coimbatore, India
4 2nd year student, Department of Computer Science and Engineering (CYBER SECURITY) Sri Shakthi Institute of Engineering and Technology Coimbatore, India
Abstract - The expanding request for personalized online shopping encounters has driven the advancement of cleverly proposal frameworks custom-made to person client inclinations. Within the design industry, a customized approach to styling proposals upgrades client fulfillment and shopping effectiveness by proposing outfits and adornments that adjust with each user's interesting characteristics, such as body shape, skin tone, stature, and fashion inclinations. In any case, conveying precise personalized proposals is challenging due to the subjective and complex nature of design, requiring a nuanced understanding and versatile reactions to client needs.This paper presents a comprehensive clothing proposal framework utilizing progressed calculations to prepare client inputs and produce custom-made equip proposals. Through a user-friendly web interface, the framework captures key individual properties and employments a generative proposal show to supply real-time, personalized styling counsel. Clients are guided on a styling travel that considers their particular highlights, making a difference them investigate closet choices that best complement their appearance. The stage coordinating item joins for a streamlined shopping involvement and utilizes MongoDB for secure information capacity, guaranteeing strong administration and assurance of client data. This approach upgrades client fulfillment and addresses the specialized challenges of personalized design proposals within the advancing online retail scene.
Key Words: content filtering, collaborative filtering, KNN, neural networks, AI-based RSs.
