A Movie recommender system based on topic modeling usingmachine learning methods

Abstract

In recent years, we have seen an increase in the production of films in a variety of categoriesand genres. Many of these products contain concepts that are inappropriate for children andadolescents. Hence, parents are concerned that their children may be exposed to these products.As a result, a smart recommendation system that provides appropriate movies based on theuser’s age range could be a useful tool for parents. Existing movie recommender systems usequantitative factors and metadata that lead to less attention being paid to the content of themovies. This research is motivated by the need to extract movie features using informationretrieval methods in order to provide effective suggestions. The goal of this study is to propose amovie recommender system based on topic modeling and text-based age ratings. The proposedmethod uses latent Dirichlet allocation (LDA) modelling to identify hidden associations betweenwords, document topics, and the levels of expression of each topic in each document. Machinelearning models are then used to recommend age-appropriate movies. It has been demonstratedthat the proposed method can determine the user’s age and recommend movies based on theuser’s age with 93% accuracy, which is highly satisfactory.