Two-sided Value-based Music Artist Recommendation in Streaming Music Services

Jing Ren*, Robert J. Kauffman, Dave King

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

20 Downloads (Pure)


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 conjunction with other factors, including time, location, external information and listening behavior.
Original languageEnglish
Title of host publicationProceedings of the 52nd Hawaii International Conference on System Sciences
EditorsTung X. Bui
Number of pages10
Place of PublicationHonolulu
PublisherHawaii International Conference on System Sciences (HICSS)
Publication date2019
ISBN (Electronic)9780998133126
Publication statusPublished - 2019
Externally publishedYes
EventThe 52nd Hawaii International Conference on System Sciences. HISS 2019: HISS 2019 - Wailea, United States
Duration: 8 Jan 201911 Jan 2019
Conference number: 52


ConferenceThe 52nd Hawaii International Conference on System Sciences. HISS 2019
Country/TerritoryUnited States
Internet address
SeriesProceedings of the Annual Hawaii International Conference on System Sciences

Cite this