International Journal of Scientific Research and Engineering Development

The movie recommendation feature aims to provide suggestions to users based on their preferred user features. A successful customer movie recommendation will, with the highest degree of performance, suggest movies with the greatest similarities. This research conducts a systematic literature review on movie recommender systems focusing on filtering criteria within the recommender systems, implemented algorithms in movie recommender systems, performance measurement criteria, challenges in implementation, and propositions for subsequent research. Detailed discussion is done on the application of some popular machine learning algorithms in movie recommender systems, including K-means clustering, self-organizing maps with principal component analysis, and principal component analysis. Research works done with metaheuristic-based recommendation systems have been given particular attention. Developments achieved in the area of designing movie recommender systems and what needs to be done to overcome the current challenge of implementing feasible solution options are the focus of this study. The article serves the broad domain of recommender systems and supports data scientists in practice who design such systems.