Experimental Investigation of Artificial Intelligence Models for Recommender Systems

The increasing amount of information and content on the Internet has made it more difficult for users to access items that may interest them. Recommender systems have emerged to determine items that may affect users by filtering information obtained from users' behaviors through certain filters and presenting them to the user. This study presents the performance of widely preferred recommendation system models such as artificial neural network, XGBoost, KNNRegressor, and LightGBM models in the related literature. The well-known datasets, namely MovieLens-100k and MovieLens-32M, are used to evaluate these models. According to the presented results, it was observed that the artificial neural network model was ahead of other models in terms of performance data.