Product Recommendation System with Explicit Feedback Using Deep Learning Methods

Today, efforts are made to develop and improve recommendation systems that will direct users to the right product according to their individual preferences during internet shopping. In this study, the recommendation system was designed with Autoencoders, which are one of the methods of deep learning and MovieLens dataset. While designing the system, various optimization algorithms, namely Gradient Descent, Gradient Descent with Momentum, RmsProp and Adam (Adaptive Momentum Optimization), were tried by using TensorFlow in the Python programming language. Moreover, the effect of increasing the amount of the data on the optimization algorithm was analyzed. Consequently, it was effectively demonstrated that the most successful one was the Adam algorithm with a test error of 1.363. It was also observed that decreasing the sparsity on the training data leads to a lower test error.