Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services REN, Jing Kauffman, Robert King, Dave 2019-01-03T00:07:00Z 2019-01-03T00:07:00Z 2019-01-08
dc.description.abstract Most 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+ 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.323
dc.identifier.isbn 978-0-9981331-2-6
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Social Media Management in Big Data Era
dc.subject Digital and Social Media
dc.subject Artist recommendation, collaborative filtering, external information, streaming music, two-sided value
dc.title Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services
dc.type Conference Paper
dc.type.dcmi Text
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