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Erschienen in:
Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation

verfasst von : Menglin Kong, Li Fan, Shengze Xu, Xingquan Li, Muzhou Hou, Cong Cao

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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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.

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Vorheriges Kapitel ScaleViz: Scaling Visualization Recommendation Models on Large Data
Nächstes Kapitel On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises
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Metadaten
Titel
Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation
verfasst von
Menglin Kong
Li Fan
Shengze Xu
Xingquan Li
Muzhou Hou
Cong Cao
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-64-8

Electronic ISBN: 978-981-9722-62-4

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-2262-4_9

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