Music Content Understanding Models forPersonalized Recommendation Systems

The rapid proliferation of digital music accessible through streaming platforms has revolutionized the way users engage withaudio content, while simultaneously introducing significant challenges for systems tasked with delivering highly personalizedlistening experiences. As millions of tracks become instantly available, identifying content that aligns with individuallistener preferences requires increasingly sophisticated techniques. Conventional recommendation methodologies, suchas collaborative filtering and content-based filtering, remain widely used but exhibit several well-documented limitations.These include cold-start issues for new users and tracks, limited capacity to surface novel or diverse content, and a lack ofdeep integration with the rich semantic and signal-level attributes inherent in musical works. To address these challenges,this study proposes a novel framework that combines advanced music content understanding with user-aware modeling toenhance recommendation accuracy and relevance. The proposed architecture integrates deep neural networks for featureextraction directly from raw audio signals, capturing timbral, rhythmic, and harmonic characteristics. In parallel, attentionmechanisms are utilized to align user preference profiles with semantically meaningful representations of musical content,allowing for fine-grained personalization. A hybrid approach blends collaborative signals—such as user co-listening patternsand implicit feedback—with content-derived embeddings to improve robustness, reduce popularity bias, and expand exposureto underrepresented tracks. Experimental evaluations conducted on benchmark music recommendation datasets demonstratethat the proposed framework significantly outperforms traditional baselines in both accuracy and personalization metrics.Notably, it excels in scenarios involving cold-start users and unseen tracks, highlighting its practical utility. This work contributesto ongoing research in music information retrieval, deep learning, and recommender systems, and provides promisingdirections for future development in human-centered media interaction and intelligent content delivery systems.