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A deep multimodal approach for cold-start music recommendation

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dc.contributor.author Oramas, Sergio
dc.contributor.author Nieto Caballero, Oriol
dc.contributor.author Sordo, Mohamed
dc.contributor.author Serra, Xavier
dc.date.accessioned 2017-12-18T09:25:49Z
dc.date.available 2017-12-18T09:25:49Z
dc.date.issued 2017
dc.identifier.citation Oramas S, Sordo M, Nieto O, Serra X. A deep multimodal approach for cold-start music recommendation. In: DLRS 2017. 2nd Workshop on Deep Learning for Recommender Systems; 2017 Aug 27; Como, Italy. New York: ACM; 2017. p. 32-7. DOI: 10.1145/3125486.3125492
dc.identifier.uri http://hdl.handle.net/10230/33519
dc.description Comunicació presentada al 2nd Workshop on Deep Learning for Recommender Systems (DLRS 2017), celebrat el 27 d'agost del 2017 a Como, Itàlia.
dc.description.abstract 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.
dc.description.sponsorship 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).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACM Association for Computer Machinery
dc.relation.ispartof DLRS 2017. 2nd Workshop on Deep Learning for Recommender Systems; 2017 Aug 27; Como, Italy. New York: ACM; 2017. p. 32-7.
dc.rights © 2017 Association for Computing Machinery
dc.title A deep multimodal approach for cold-start music recommendation
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1145/3125486.3125492
dc.subject.keyword Recommender systems
dc.subject.keyword Deep learning
dc.subject.keyword Multimodal
dc.subject.keyword Music
dc.subject.keyword Semantics
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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