SISTEM REKOMENDASI ALTERNATIF MUSIK BERDASARKAN MOOD USER MENGGUNAKAN METODE CONTENT BASED FILTERING - Syamil Taqiyuddin Ayyasy (2023)
This research aims to generate personalized music alternative recommendations based on the user's mood. Content-Based Filtering is used to create this recommendation system. This method utilizes various models to find similarities between data or documents to produce meaningful recommendations. One of the models used is Term Frequency Inverse Document Frequency (TF-IDF). Following this, Cosine Similarity is used in the text classification domain to indicate the level of similarity between two documents. This research yields a simulation system that can recommend alternative music choices based on the user's mood, with an Average Precision@10 score of 0.7207 and an Average Recall@10 score of 0.9896. This implies that the system can provide fairly relevant recommendations. In conclusion, this recommendation system can assist users in selecting music that aligns with their mood, thereby enhancing their music listening experience.