Abstract: In the twenty-first century, amidst an overwhelming amount of data, the task of finding personally relevant content, particularly in the realm of movies, has become increasingly difficult. To address this issue, movie recommendation systems have emerged as indispensable tools, with the goal of making it easier to choose movies from a large number of options. This paper proposes a content-based approach to movie recommendation that uses machine learning to analyze movie attributes like genres, directors, actors, and plot keywords. By parsing and transforming movie metadata into meaningful representations, our system aims to provide personalized movie recommendations based on individual preferences. Using a variety of datasets, including important metadata such as actors, directors, and genres, we use algorithms such as Text Vectorization and Cosine Similarity to generate recommendations based on each movie's unique characteristics. This content-based filtering approach provides users with a personalized and enriching movie selection experience, addressing the issue of choice overload in the media environment of today.
Keywords: Movie Recommendation System, Recommendation Systems, Content-Based Approaches, cosine similarity
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DOI:
10.17148/IJARCCE.2024.13408
