Sulistyo. S, Dian (2023) Implementasi Metode Content-based Filtering Pada Sistem Rekomendasi Film. Other thesis, Universitas Islam Riau.

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Abstract

Currently, movies are the most popular form of entertainment worldwide. With an increasing number of films released every year, the use of movie recommendation systems has become increasingly important for users. Movie recommendation systems can assist users in finding films that match their preferences more quickly and efficiently. Movies generate a large amount of data, including movie metadata, synopses, user reviews, and audience preferences. Proper data analysis can provide valuable insights into user trends and preferences in films. By implementing content-based filtering methods, researchers leverage this vast amount of data to create more accurate and personalized recommendations. This application is built using content-based filtering methods and cosine similarity algorithms to compute film similarity within it. The application has been tested through 5 experiments, achieving a high level of precision, recall, and f1-score, approximately 100% for precision, 84% for recall, and 91% for f1-score. Therefore, based on these results, this system meets the criteria for implementation.

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