Generating interesting song-to-song segues with Dave

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dc.contributor.author Gabbolini, Giovanni
dc.contributor.author Bridge, Derek G.
dc.date.accessioned 2021-08-03T12:08:04Z
dc.date.available 2021-08-03T12:08:04Z
dc.date.issued 2021-06-21
dc.identifier.citation Gabbolini, G. and Bridge, D. (2021) 'Generating interesting song-to-song segues with Dave', UMAP 2021 - Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Utrecht, Netherlands, 21-25 June, pp. 98-107. doi: 10.1145/3450613.3456819 en
dc.identifier.startpage 98 en
dc.identifier.endpage 107 en
dc.identifier.isbn 978-1-4503-8366-0
dc.identifier.uri http://hdl.handle.net/10468/11632
dc.identifier.doi 10.1145/3450613.3456819 en
dc.description.abstract We introduce a novel domain-independent algorithm for generating interesting item-to-item textual connections, or segues. Pivotal to our contribution is the introduction of a scoring function for segues, based on their ‘interestingness’. We provide an implementation of our algorithm in the music domain. We refer to our implementation as Dave. Dave is able to generate 1553 different types of segues, that can be broadly categorized as either informative or funny. We evaluate Dave by comparing it against a curated source of song-to-song segues, called The Chain. In the case of informative segues, we find that Dave can produce segues of the same quality, if not better, than those to be found in The Chain. And, we report positive correlation between the values produced by our scoring function and human perceptions of segue quality. The results highlight the validity of our method, and open future directions in the application of segues to recommender systems research. en
dc.description.sponsorship Science Foundation Ireland (Grant number 12/RC/2289-P2) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery en
dc.relation.uri https://www.um.org/umap2021/
dc.rights © 2021, Association for Computing Machinery. The definitive Version of Record was published in UMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization: https://doi.org/10.1145/3450613.3456819. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to this Author Accepted Manuscript. en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/ en
dc.subject Interestingness en
dc.subject Recommender systems en
dc.subject Segues en
dc.subject User studies en
dc.title Generating interesting song-to-song segues with Dave en
dc.type Conference item en
dc.internal.authorcontactother Giovanni Gabbolini, Insight Centre for Data Analytics, University College Cork, Cork, Ireland. . T: +353-21- 490-3000 E: giovanni.gabbolini@insight-centre.org en
dc.internal.availability Full text available en
dc.date.updated 2021-08-03T11:23:45Z
dc.description.version Accepted Version en
dc.internal.rssid 576625702
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Utrecht, Netherlands en
dc.internal.IRISemailaddress giovanni.gabbolini@insight-centre.org en


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© 2021, Association for Computing Machinery. The definitive Version of Record was published in UMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization: https://doi.org/10.1145/3450613.3456819. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to this Author Accepted Manuscript. Except where otherwise noted, this item's license is described as © 2021, Association for Computing Machinery. The definitive Version of Record was published in UMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization: https://doi.org/10.1145/3450613.3456819. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to this Author Accepted Manuscript.
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