Personalized Content-based Movie Recommendation System: A Comparative Analysis

A recommendation system is an analytical tool designed to provide personalized suggestions to users for specific resources such as literature, films, or music, derived from a comprehensive dataset. Movie recommendation systems aim to predict movies a user might enjoy based on attributes of previously liked movies. These systems are particularly advantageous for organizations that manage data from extensive customer bases and seek to deliver optimal suggestions. Factors such as genre, cast, and filmmakers significantly influence recommendations, which may be based on a single attribute or a combination of features. This paper focuses on a genre-based recommendation system employing content-based filtering to cater to user preferences. Using Python, the design tools algorithms like Cosine Similarity, Jaccard Similarity, and Pearson Correlation on a large dataset to identify patterns and preferences eIectively. Through a detailed evaluation of these approaches, the system provides robust and applicable movie suggestions. By smoothing user-friendly navigation and aligning recommendations with user interests, the system enhances user engagement and satisfaction. Also, this study examines the strengths and limitations of the applied algorithms, emphasizing the implicit for combining multiple similarity measures to enhance accuracy. The findings emphasize the significance of substantiated content delivery in optimizing user experience.