<![CDATA[The recommendation system is a crucial element in various digital platforms, particularly within the entertainment industry. Its presence helps users discover films that align with their preferences. As the popularity of digital platforms continues to rise in the modern era, the main challenge lies in meeting users’ needs for relevant recommendations amid the diversity and ever-increasing volume of available content. This study focuses on a literature review to determine the most suitable methods to be applied in movie recommendation systems. The urgency of this research lies in the importance of a platform’s ability to provide recommendations that are not only relevant but also capable of enhancing user engagement and satisfaction. The proposed solution in this study involves applying methods that can analyze user preferences and behavior to improve the accuracy and level of personalization within the recommendation system. The research employs the Systematic Literature Review (SLR) method by collecting articles published between 2020 and 2024 from the Google Scholar database, all of which are relevant to the topic of movie recommendation systems. From the search results, 20 selected articles were used as the basis for analysis. Based on the analysis of these articles, it was found that up until the end of 2024, the most widely used method in movie recommendation systems is Collaborative Filtering, achieving the highest precision rate of 89% and a recall value of 96%.]]>
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