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Neural content-aware collaborative filtering for cold-start music recommendation

Paul Magron 1 Cédric Févotte 1
1 IRIT-SC - Signal et Communications
IRIT - Institut de recherche en informatique de Toulouse
Abstract : State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data. These approaches inherently face the cold-start problem, as they cannot recommend novel songs with no listening history. Content-aware recommendation addresses this issue by incorporating content information about the songs on top of collaborative filtering. However, methods falling in this category rely on a shallow user/item interaction that originates from a matrix factorization framework. In this work, we introduce neural content-aware collaborative filtering, a unified framework which alleviates these limits, and extends the recently introduced neural collaborative filtering to its content-aware counterpart. We propose a generative model which leverages deep learning for both extracting content information from low-level acoustic features and for modeling the interaction between users and songs embeddings. The deep content feature extractor can either directly predict the item embedding, or serve as a regularization prior, yielding two variants (strict and relaxed) of our model. Experimental results show that the proposed method reaches state-of-the-art results for a cold-start music recommendation task. We notably observe that exploiting deep neural networks for learning refined user/item interactions outperforms approaches using a more simple interaction model in a content-aware framework.
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Preprints, Working Papers, ...
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Contributor : Paul Magron Connect in order to contact the contributor
Submitted on : Monday, October 11, 2021 - 3:39:55 PM
Last modification on : Tuesday, October 19, 2021 - 2:45:42 PM


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  • HAL Id : hal-03152158, version 1
  • ARXIV : 2102.12369


Paul Magron, Cédric Févotte. Neural content-aware collaborative filtering for cold-start music recommendation. 2021. ⟨hal-03152158⟩



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