Matrix Factorization Using LightFM fora Music Recommendation System Based on Emotional and Listening Behavior Awareness

Matrix Factorization Using LightFM fora Music Recommendation System Based on Emotional and Listening Behavior Awareness

Music Recommendation, LightFM, Hybrid Matrix Factorization, EmotionState, Listening Behaviour, K-Means

This paper summarizes an emotion-aware hybrid music recommendation approach that combines users’ emotional listening characteristics and implicit behavioral signals to enhance personalization under sparse feedback conditions. We construct enriched user profiles by aggregating audio-derived emotional features such as valence and danceability, modeling genre preferences through one-hot encoding, and capturing engagement behavior via skip-rate statistics, followed by systematic preprocessing including outlier removal and normalization. Using standardized emotional features, we apply K-means clustering to assign interpretable mood contexts (e.g., happy, energetic, calm, and sad), which are then incorporated as user-aware signals in a hybrid LightFM matrix factorization model optimized with the WARP-kos loss for ranking-based recommendation. Experimental evaluation demonstrates that the proposed model achieves a Precision@10 of 0.6209, indicating that more than six out of ten recommended tracks are relevant, and aRecall@10 of 0.4663, meaning that approximately 47% of all relevant items are successfully retrieved within the top-10 recommendations. These results highlight the model’s ability to balance accuracy and coverage while outperforming traditional collaborative and content-based baselines, thereby confirming that integrating emotional context with behavioral data significantly improves the effectiveness of personalized music recommendation systems

Anindita Putri Dayati1, Indriani2

https://ijicom.respati.ac.id/index.php/ijicom/article/view/197/140

International Journal of Informatics and Computation (IJICOM)

2025

Fajar bagus W

PDF

English

Text