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Exploring gaps in group recommendation research

Master Thesis (2021)
Author(s)

A.H.J. Bánsági (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Cynthia C. S. Liem – Mentor (TU Delft - Multimedia Computing)

A Hanjalic – Graduation committee member (TU Delft - Intelligent Systems)

Jie Yang – Graduation committee member (TU Delft - Web Information Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Aurél Bánsági
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Aurél Bánsági
Graduation Date
27-08-2021
Awarding Institution
Delft University of Technology
Related content

Link to the code used for the experiments and the datasets collected during the experiment.

https://github.com/Austaon/GroupRecommendationThesis
Faculty
Electrical Engineering, Mathematics and Computer Science
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

In group recommendation, a key question is how preferences from individuals should be obtained and then aggregated into a group outcome. Collecting individual preferences can be done through implicit or explicit means, but there is insufficient research available on what option is optimal. For comparing different possible aggregation strategies, much of the existing research in the field departs from existing preference data (e.g.\ ratings), but considers synthetically created groups, rather than real groups. This study describes two experiments focusing on these issues. The first compared historical listening data with explicitly provided data and showed them to be similar. The second experiment compares different aggregation strategies in a more ecologically valid setting. More specifically, it considers playlist creation scenarios for groups, in which participants were asked to create their own, real-life groups. While the playlists as a whole did not show any significant differences in performance, inspecting the ratings of the tracks did show some differences. Finally, acoustic similarity was explored using the data obtained in the experiments. Some early results were observed, but more research is needed in this area.

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ThesisAurelBansagi.pdf
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