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Stylesync: Smart Outfit Recommendations
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 10 |
| Year of Publication: 2025 |
| Authors: Aditya Mangesh Kotkar, Sahil Anil Panchal, Pratik Ramesh Gupta, Rupali Sunil Rana, Shubhangi Chavan |
10.5120/ijca2025924878
|
Aditya Mangesh Kotkar, Sahil Anil Panchal, Pratik Ramesh Gupta, Rupali Sunil Rana, Shubhangi Chavan . Stylesync: Smart Outfit Recommendations. International Journal of Computer Applications. 187, 10 ( Jun 2025), 1-15. DOI=10.5120/ijca2025924878
Abstract
Our "StyleSync: Outfit Recommender" project is an innovative initiative that leverages Machine Learning (ML), Deep Learning (DL), and advanced algorithms to transform the fashion industry. By combining cutting-edge technologies with creative solutions, this project is designed to offer personalized outfit recommendations that align with each user's individual preferences and style. The system will utilize ML techniques like collaborative filtering, clustering, and decision trees to analyze user data, past fashion trends, and personal style profiles, ensuring accurate and relevant outfit suggestions. Deep Learning methods such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) will be applied to extract complex patterns from images, allowing the system to understand visual appeal and recommend outfits that are aesthetically pleasing. Additionally, the project will explore the use of reinforcement learning algorithms to improve outfit recommendations over time, based on user feedback and interactions. By continuously adapting to user preferences, the system will enhance its recommendations, providing a more tailored and engaging experience.
References
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