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Title:
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A deep multimodal approach for cold-start music recommendation
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Author:
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Oramas, Sergio; Nieto Caballero, Oriol; Sordo, Mohamed; Serra, Xavier
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Abstract:
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Comunicació presentada al 2nd Workshop on Deep Learning for Recommender Systems (DLRS 2017), celebrat el 27 d'agost del 2017 a Como, Itàlia. |
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Abstract:
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An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce? In this work we aim to address this so-called cold-start problem by combining text and audio information with user feedback data using deep network architectures. Our method is divided into three steps. First, artist embeddings are learned from biographies by combining semantics, text features, and aggregated usage data. Second, track embeddings are learned from the audio signal and available feedback data. Finally, artist and track embeddings are combined in a multimodal network. Results suggest that both splitting the recommendation problem between feature levels (i.e., artist metadata and audio track), and merging feature embeddings in a multimodal approach improve the accuracy of the recommendations. |
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Abstract:
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This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). |
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Subject(s):
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-Recommender systems
-Deep learning
-Multimodal
-Music
-Semantics |
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Rights:
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© 2017 Association for Computing Machinery
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Document type:
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Conference Object
Article - Accepted version |
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Published by:
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ACM Association for Computer Machinery
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