Implementation of Content-Based Cosine Similarity Algorithm with TF-IDF and SBERT for Movie Recommendation - Eliata Zefanya Irabela; Norhikmah (APIC, 2026)
The number of films continues to increase on streaming platforms often makes users confused in deciding which film to watch. To overcome this research develops content-based movie recommendation system. Representation of the film information obtained through the application of TF-IDF and SBERT to genre and synopsis data. Cosine similarity is used to calculate the closeness between representations. The performance system is then evaluated through the Precision@K, MAP@K, and Recall@K metrics. From the test results, hybrid approach shows better performance more stable than single method. With a MAP value reaching 0.95 Recall 0.95 dan Precission 0.71 . In the future, the development system will still possible by utilizing other types of data, including user interaction data.