• Aarti Bobade
• Bindu Udar
• Nitya Gupta
• Snehal Kahar
• Meera Sawalkar

: A recommender system primary purpose is to provide a series of item suggestions on a topic to its user. Deep learning is used in many fields and solved difficult and complex problems with large volumes of data. Deep learning can also be used in referral systems. Today, online shopping systems are looking for a method that can recommend items according to the user preference and interest in order to increase their sales. Clothing sales systems offer a set of recommendation based on the needs and interests of the users. Now-a-days, nearly all of the tasks are being done online. People prefer to use online which is why we propose a content-based clothing recommender system using deep neural network. In content based systems, product features are required for prediction of unobserved items ratings. In our proposed system by using a deep neural network, the cloth category is obtained and the need to manually extract the product features is eliminated by producing the required features with a large and useful volume. The advantage of this system is that it uses the same network to specify gender as a feature in making suggestions then shows the results to the user. Different machine learning algorithms are tested and analysed with and without considering demographic information such as gender. The experimental results show that the loss of our proposed system is lower than the other related systems and solves the cold start problem for new items. Our proposed system also recommends novel, relevant and unexpected items.

Clothing, Recommender System, Deep learning, Demographic, Feature Extraction, Cold start, Content, Coronavirus

"FASHION RECOMMENDATION SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.m650-m655, May-2023, Available :http://www.jetir.org/papers/JETIR2305C92.pdf

"FASHION RECOMMENDATION SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppm650-m655, May-2023, Available at : http://www.jetir.org/papers/JETIR2305C92.pdf