Generating interesting song-to-song segues with Dave

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Date
2021-06-21
Authors
Gabbolini, Giovanni
Bridge, Derek G.
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Volume Title
Publisher
Association for Computing Machinery
Published Version
10.1145/3450613.3456819
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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.
Description
Keywords
Interestingness , Recommender systems , Segues , User studies
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
Link to publisher’s version
https://www.um.org/umap2021/
URI
https://hdl.handle.net/10468/11632
Collections
Insight Centre for Data Analytics - Conference Items
Computer Science - Conference Items
Copyright
© 2021, the Authors. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to this Author Accepted Manuscript. 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.