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@article{163209,
        author = {Tamish Gambhir and Sneha Singh and Sandeep Kumar},
        title = {Personalized Outfit Recommendation System Using Collaborative Filtering and Content Based Filtering},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {1478-1488},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163209},
        abstract = {The surge in e-commerce and the growing demand for personalized shopping experiences have heightened interest in fashion recommendation systems. This study introduces an innovative outfit recommendation system that tailors suggestions based on user choices and machine fashion methods. The primary objective is to enhance users' fashion decisions by considering variables such as style, occasion, weather, and personal preferences. At the system's core is a robust machine fashion model, integrating comprehensive knowledge of clothing products and fashion trends. This model discerns relationships and style patterns among various clothing items by analyzing vast collections of fashion photographs. Additionally, it incorporates real-time meteorological data to offer weather- appropriate recommendations, ensuring users' comfort and style Personalized recommendations heavily rely on user preferences. The system continually refines suggestions through iterative improvement. gathering user feedback and considering previous selections. Collaborative filtering techniques and sentiment analysis are employed to comprehend user preferences and enhance recommendation accuracy over time. Users input the event and their preferred style to receive wardrobe recommendations tailored to their individual tastes. This research study presents an outfit recommendation system offering a comprehensive solution to enhance the fashion buying experience. By leveraging machine fashion algorithms, human preferences, and real-time weather data, the system empowers users to make stylish and well-informed outfit choices. Its flexibility and responsiveness to user input position it as a valuable resource for fashion-conscious consumers and a promising subject for further research in the field of fashion.},
        keywords = {Content Filtering, Large Language Models (LLM), Collaborative Filtering},
        month = {},
        }