Movie Recommendation Systems through Genre Correlation-Based Content and Collaborative Filtering
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Abstract
Recommendation systems play a pivotal role in suggesting resources such as books, movies, songs, and more to users based on data analysis. Movie recommendation systems, in particular, predict a user's preferences for movies by evaluating attributes found in their previously favored films. These systems are invaluable for organizations amassing data from numerous customers, aiming to deliver optimal suggestions. Various factors can influence the design of a movie recommendation system, including genre, actors, and directors. Recommendations can be made based on one attribute or a combination of multiple attributes. This paper presents a recommendation system that focuses on users' preferred movie genres. The approach employs content-based and collaborative-based filtering using genre correlation and utilizes the Movie Lens dataset.
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