A user-centered investigation of personal music tours

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dc.contributor.authorGabbolini, Giovanni
dc.contributor.authorBridge, Derek G.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2023-01-13T14:38:40Z
dc.date.available2023-01-13T14:38:40Z
dc.date.issued2022-09-18
dc.date.updated2023-01-13T13:00:45Z
dc.description.abstractStreaming services use recommender systems to surface the right music to users. Playlists are a popular way to present music in a list-like fashion, i.e. as a plain list of songs. An alternative are tours, where the songs alternate with segues, which explain the connections between consecutive songs. Tours address the user need of seeking background information about songs, and are found to be superior to playlists, given the right user context. In this work, we provide, for the first time, a user-centered evaluation of two tour-generation algorithms (Greedy and Optimal) using semi-structured interviews. We assess the algorithms, we discuss attributes of the tours that the algorithms produce, we identify which attributes are desirable and which are not, and we enumerate several possible improvements to the algorithms, along with practical suggestions on how to implement the improvements. Our main findings are that Greedy generates more likeable tours than Optimal, and that three important attributes of tours are segue diversity, song arrangement and song familiarity. More generally, we provide insights into how to present music to users, which could inform the design of user-centered recommender systems.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGabbolini, G. and Bridge, D. (2022) ‘A user-centered investigation of personal music tours’, Sixteenth ACM Conference on Recommender Systems (RecSys '22), Seattle, WA, USA, 18-23 Sept. ACM, New York, NY, USA: ACM, pp. 25-34. doi: 10.1145/3523227.3546776en
dc.identifier.doi10.1145/3523227.3546776en
dc.identifier.endpage34en
dc.identifier.isbn978-1-4503-9278-5
dc.identifier.startpage25en
dc.identifier.urihttps://hdl.handle.net/10468/14048
dc.language.isoenen
dc.publisherACMen
dc.relation.ispartofRecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://doi.org/10.1145/3523227.3546776
dc.rights© 2022 Copyright held by the owner/author(s). This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 12/RC/2289-P2 which is co-funded under the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectMusic recommender systemsen
dc.subjectPlaylistsen
dc.subjectSeguesen
dc.subjectUser evaluation.en
dc.titleA user-centered investigation of personal music toursen
dc.typeConference itemen
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