Abstract: For many applications, particularly in the academic environment and industry, the Recommendation System for Technical Paper Reviewers is very important. This study examines the research trends connecting the highly technical components of recommendation systems employed in various service fields to their commercial aspects. It is a technique that enables the user to identify the information that will be useful to him or her from the variety of facts accessible. In terms of the movie recommendation system, recommendations are made either based on user similarities in collaborative filtering or by considering the user's intended engagement with the content into account content-based filtering. A stronger recommendation system is produced by combining content-based and collaborative filtering, which overcomes the issues that collaborative and content-based filtering typically have. The similarity between users is also determined using a variety of similarity measures in order to make recommendations. We have reviewed cutting-edge approaches to collaborative filtering, content-based filtering, deep learning-based methods, and hybrid approaches in this study for movie recommendation. Additionally, we looked at other similarity measures. Numerous businesses, including Facebook, which suggests friends, LinkedIn, which suggests jobs, Pandora, which suggests music, Netflix, which suggests movies, and Amazon, which suggests purchases, among others, employ recommendation systems to boost their profits and help their clients. This essay primarily focuses on providing a succinct overview of the many approaches and techniques used for movie recommendation in order to investigate the field of recommendation systems research.