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- W4286607675 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." @default.
- W4286607675 created "2022-07-22" @default.
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- W4286607675 date "2021-02-24" @default.
- W4286607675 modified "2023-09-25" @default.
- W4286607675 title "Neural content-aware collaborative filtering for cold-start music recommendation" @default.
- W4286607675 doi "https://doi.org/10.48550/arxiv.2102.12369" @default.
- W4286607675 hasPublicationYear "2021" @default.
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