A Content-Based Recommendation System Using Neuro-Fuzzy Approach

In this paper, we present our novel approach to recommender systems based on a neuro-fuzzy approach. The neuro-fuzzy approach allows for deciding to recommend or not to recommend processed items for a user. By using it, we can understand the decision through analyzing rules of decision paths. Our method gives a possibility to learn and simulate users decisions based on their actions in our test environment. Finally, a rank list of top-rated items is delivered to the user based on simulated rank for each of them. We develop our AI framework to perform tests with the use of CUDA technology. Additionally, we develop a user interface in the form of a web application. It gives the possibility to perform simulations of real users. To compare our approach with a deep learning based method, we perform tests on the MovieLens 20M Dataset. It should be noted that the architecture of the data module of our system allowed for reasonably easy integration with MovieLens data.