Content-based Clothing Recommender System using Deep Neural Network

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. Today, due to the current situation caused by the Coronavirus, the majority of tasks are done online. In this paper, 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 analyzed 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.