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Characterisation of explicit feedback in an online music recommendation service

Authors:

Gawesh Jawaheer,

Martin Szomszor,

Patty KostkovaAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 317 - 320

https://doi.org/10.1145/1864708.1864776

Published: 26 September 2010 Publication History

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Abstract

In this paper, we present our study and characterisation of explicit and implicit feedback on Last.fm, an online music station and recommender service. The dataset consisted of explicit positive feedback (through loved tracks) and implicit positive feedback (the number of times a track is played). As one would expect, our analysis shows that explicit feedback is very scarce. However, we also found that the rate at which a user provides explicit feedback decreases with time, and that overall leaving explicit feedback has a negative effect on the user's behaviour.

References

[1]

}}Amatriain, X., Pujol, J., Tintarev, N., and Oliver, N. Rate it again: increasing recommendation accuracy by user re-rating. Proceedings of the third ACM conference on Recommender systems, ACM (2009), 173--180.

Digital Library

Google Scholar

[2]

}}Anand, S. S., Kearney, P., and Shapcott, M. Generating semantically enriched user profiles for Web personalization. ACM Transactions on Internet Technology 7, 4 (2007), 22-es.

Digital Library

Google Scholar

[3]

}}Kelly, D. and Teevan, J. Implicit feedback for inferring user preference: a bibliography. ACM SIGIR Forum, (2003).

Digital Library

Google Scholar

[4]

}}O'Mahony, M. P., Hurley, N. J., and Silvestre, G. C. Detecting noise in recommender system databases. Proceedings of the 11th international conference on Intelligent user interfaces - IUI '06, (2006), 109.

Digital Library

Google Scholar

[5]

}}Teevan, J., Dumais, S., Horvitz, E., and others. Potential for Personalization. ACM Transactions on Computer-Human Interaction 1, 212 (2008), 1--35.

Digital Library

Google Scholar

[6]

}}White, R., Jose, J., and Ruthven, I. Comparing explicit and implicit feedback techniques for web retrieval: Trec-10 interactive track report. NIST SPECIAL PUBLICATION SP, (2002), 534--538.

Google Scholar

Cited By

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Tsubouchi KYamaguchi SHigurashi T(2023)Boosting Feedback: A Framework for Enhancing Ground Truth Data Collection2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386658(1982-1989)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386658
Ogunseyi TAvoussoukpo CJiang Y(2023)A systematic review of privacy techniques in recommendation systemsInternational Journal of Information Security10.1007/s10207-023-00710-122:6(1651-1664)Online publication date: 5-Jun-2023
https://doi.org/10.1007/s10207-023-00710-1
Shibata HEbine KTakama Y(2023)Method to Control Embedded Representation of Piece of Music in PlaylistsAdvanced Computational Intelligence and Intelligent Informatics10.1007/978-981-99-7590-7_19(226-240)Online publication date: 30-Oct-2023
https://doi.org/10.1007/978-981-99-7590-7_19
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Index Terms

Characterisation of explicit feedback in an online music recommendation service
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Tsubouchi KYamaguchi SHigurashi T(2023)Boosting Feedback: A Framework for Enhancing Ground Truth Data Collection2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386658(1982-1989)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386658
Ogunseyi TAvoussoukpo CJiang Y(2023)A systematic review of privacy techniques in recommendation systemsInternational Journal of Information Security10.1007/s10207-023-00710-122:6(1651-1664)Online publication date: 5-Jun-2023
https://doi.org/10.1007/s10207-023-00710-1
Shibata HEbine KTakama Y(2023)Method to Control Embedded Representation of Piece of Music in PlaylistsAdvanced Computational Intelligence and Intelligent Informatics10.1007/978-981-99-7590-7_19(226-240)Online publication date: 30-Oct-2023
https://doi.org/10.1007/978-981-99-7590-7_19
Fan HWu KParvin HBeigi APho K(2021)A Hybrid Recommender System Using KNN and ClusteringInternational Journal of Information Technology & Decision Making10.1142/S021962202150005X20:02(553-596)Online publication date: 31-Mar-2021
https://doi.org/10.1142/S021962202150005X
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Ramampiaro HLangseth HAlmenningen TSchistad HHavig MNguyen H(2019)New Ideas in Ranking for Personalized Fashion Recommender SystemsBusiness and Consumer Analytics: New Ideas10.1007/978-3-030-06222-4_25(933-961)Online publication date: 31-May-2019
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Hu DLouca RHong LMcAuley JPera SEkstrand MAmatriain XO'Donovan J(2018)Learning within-session budgets from browsing trajectoriesProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240401(432-436)Online publication date: 27-Sep-2018
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Nguyen VHuynh V(2016)On Information Fusion in Recommender Systems Based on Dempster-Shafer Theory2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2016.0022(78-85)Online publication date: Nov-2016
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