Music Recommendation System

Digital music platforms provide millions of tracks, making it difficult for listeners to discover songs that match their interests,current mood, and listening context. A music recommendation system addresses this information-overload problem by rankingtracks likely to be relevant to an individual user. This paper presents a proposed hybrid recommendation system combiningcontent-based filtering and collaborative filtering. Content-based features represent songs using metadata such as genre, artist,language, tempo, and audio descriptors, while collaborative filtering uses user–track interactions such as plays, likes, skips, andplaylist additions. A ranking component combines these signals and applies basic constraints to reduce repetition and improvediversity. The proposed architecture includes data collection, preprocessing, feature representation, candidate generation, ranking,feedback capture, and a user interface. The methodology describes baseline models and evaluation using Precision@K, Recall@K,NDCG@K, catalog coverage, and user feedback. No experimental performance results are claimed because the proposed systemhas not been implemented and tested as part of this paper. Privacy, cold-start handling, popularity bias, and transparency arediscussed as key design concerns