Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services

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dc.contributor.authorREN, Jing
dc.contributor.authorKauffman, Robert
dc.contributor.authorKing, Dave
dc.date.accessioned2019-01-03T00:07:00Z
dc.date.available2019-01-03T00:07:00Z
dc.date.issued2019-01-08
dc.description.abstractMost work on music recommendations has focused on the consumer side not the provider side. We develop a two-sided value-based approach to music artist recommendation for a streaming music scenario. It combines the value yielded for the music industry and consumers in an integrated model. For the industry, the approach aims to increase the conversion rate of potential listeners to adopters, which produces new revenue. For consumers, it aims to improve their utility related to recommendations they receive. We use one year of listening records for 15,000+ Last.fm users to train and test the proposed recommendation model on 143 artists. Compared to collaborative filtering, the results show some improvement in recommendation performance by considering both sides’ value in con-junction with other factors, including time, location, external information and listening behavior.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.323
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59705
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSocial Media Management in Big Data Era
dc.subjectDigital and Social Media
dc.subjectArtist recommendation, collaborative filtering, external information, streaming music, two-sided value
dc.titleTwo-Sided Value-Based Music Artist Recommendation in Streaming Music Services
dc.typeConference Paper
dc.type.dcmiText

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