Performance Comparison of Machine Learning Algorithms in Movie Recommender Systems

We are all aware that the use of technology in every domain of life produces an enormous amount of information by overloading the amount of data on the Internet. To make data access easier, recommendation systems have been shown to be more efficient, especially performance enhancement has been significantly increased with the integration and use of machine learning algorithms. This paper compares the performance of three machine learning algorithms: Naïve Bayes, neural networks and logistic regression when applied on a movie recommender system. The movie recommender system is implemented in Python programming language using the MovieLens dataset.