Abstract
Personalized music recommendation technology is effective in helping users discover desired songs. However, accurate recommendations become challenging in cold-start scenarios with newly registered or limited data users. To address the accuracy, diversity, and interpretability challenges in cold-start music recommendation, we propose CFLS, a novel approach that conducts collaborative filtering in the space of latent variables based on the Variational Auto-Encoder (VAE) framework. CFLS replaces the standard normal distribution prior in VAE with a Gaussian process (GP) prior based on user profile information, enabling consideration of user correlations in the latent space. Experimental results on real-world datasets demonstrate the effectiveness and superiority of our proposed method. Visualization techniques are employed to showcase the diversity, interpretability, and user-controllability of the recommendation results achieved by CFLS.
Menglin Kong and Li Fan contributed equally to this work.
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Acknowledgements
This study was supported by Natural Science Foundation of Hunan Province (grant number 2022JJ30673) and by the Graduate Innovation Project of Central South University (2023XQLH032, 2023ZZTS0304).
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Kong, M., Fan, L., Xu, S., Li, X., Hou, M., Cao, C. (2024). Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_9
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