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Unboxing the Algorithm: Designing an Understandable Algorithmic Experience in Music Recommender Systems
Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3).
Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-1852-3937
2021 (English)In: Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021. co-located with the 15th ACM Conference on Recommender Systems (RecSys 2021)., 2021Conference paper, Published paper (Refereed)
Abstract [en]

After decades of the existence of algorithms in everyday use technologies, users have developed an algorithmic awareness, but they still lack the confidence to grasp them. This study explores how understandability as a principle drawn from sociology, design, and computing can enhance the algorithmic experience in music recommendation systems. The preliminary results of this Research-Through-Design showed that users had limited mental models so far but had a curiosity to learn. Further, it confirmed that explanations as a dialogue could improve the algorithmic experience in music recommendation systems. Users could comprehend recommendations the best when they were easy to access and understand, directly related to user behavior, and when they allowed the user to correct the algorithm. To conclude, our study reconfirms that designing experiences that help users to understand the algorithmic workings will make authentic recommendations from intelligent systems more applicable in the long run.

Place, publisher, year, edition, pages
2021.
Keywords [en]
Human-centered computing, Interaction design, Empirical studies in interaction design, algorithmic experience, music recommendation systems, transparency, machine learning, explainable AI
National Category
Computer Systems Design Human Computer Interaction
Research subject
Interaktionsdesign
Identifiers
URN: urn:nbn:se:mau:diva-46098OAI: oai:DiVA.org:mau-46098DiVA, id: diva2:1597334
Conference
15th ACM Conference on Recommender Systems (RecSys 2021), Amsterdam, The Netherlands, September 25, 2021.
Available from: 2021-09-25 Created: 2021-09-25 Last updated: 2023-03-13Bibliographically approved

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Unboxing Algorithm(1664 kB)390 downloads
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Ghajargar, Maliheh

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CiteExportLink to record
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Citation style
  • apa
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