Outfit Recommendation Chatbot (Lumin)

Abstract

"Outfit recommendation systems can only be as good as the recommendations that they provide; this makes accuracy and elements of personalisation critical in such systems. However, such systems are not easy to develop because fashion preferences are diverse and complex, and to train models to capture such complexities, high-quality data are often required in large amounts. The generation of outfit recommendations using classical approaches depends on intensive human interference or expertise. In this study, the author also demonstrates an intention to make automatic outfit recommendation to enhance the performance using the machine learning techniques and RASA framework. To this end, the author proposed and implemented a chatbot system having the ability to take into account user preferences as well as context and current fashion trends all through the help of neural networks and natural language processing and the entire architecture was built using RASA. This was complemented by a new form of recommendation system that can adapt to receive feedback from the users and changing data on fashion trends. This approach addresses the existing research gap because it enhances at once the precision and flexibility of the recommendations. The findings of the performed experiments indicate that the outfit recommendation chatbot contributes to the overall user experience as it provides accurate and punctual fashion suggestions. It is effective in dealing with the user inputs in order to come up with the preferred outfits hence the high levels of engagement and satisfaction. As a result of the proposed system, users get the suitable fashion recommendation, which takes into account not only their current needs and preferences but also potential changing factors. This shows how the fashion industry could be revolutionised when analysed with self-improving bots based on the RASA platform like the current chatbots."