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Building Public Service Recommenders: Logbook of a Journey

Published: 13 September 2021 Publication History
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References

[1]
Balázs Bodó, Natali Helberger, Sarah Eskens, and Judith Möller. 2019. Interested in diversity: The role of user attitudes, algorithmic feedback loops, and policy in news personalization. Digital Journalism 7, 2 (2019), 206–229.
[2]
British Broadcasting Corporation. 2021. About the BBC – Global news services.https://www.bbc.com/aboutthebbc/whatwedo/worldservice.
[3]
British Broadcasting Corporation. 2021. About the BBC – Mission, values, and public purposes.https://www.bbc.com/aboutthebbc/governance/mission.
[4]
British Broadcasting Corporation. 2021. About the BBC – The BBC’s services in the UK.https://www.bbc.com/aboutthebbc/whatwedo/publicservices.
[5]
British Broadcasting Corporation. 2021. BBC – Editorial guidelines.https://www.bbc.co.uk/editorialguidelines/.
[6]
British Broadcasting Corporation. 2021. Responsible technical development. http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/artificial-intelligence-committee/artificial-intelligence/written/70493.html.
[7]
Myrna Macgregor. 2021. Responsible AI at the BBC: Our Machine Learning Engine Principles. https://www.bbc.co.uk/rd/publications/responsible-ai-at-the-bbc-our-machine-learning-engine-principles.
[8]
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635(2019).

Cited By

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  • (2024)Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural CitizenshipACM Transactions on Recommender Systems10.1145/36431382:1(1-32)Online publication date: 7-Mar-2024
  • (2024)RADio* – An Introduction to Measuring Normative Diversity in News RecommendationsACM Transactions on Recommender Systems10.1145/36364653:1(1-29)Online publication date: 2-Aug-2024
  • (2024)Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658926(573-584)Online publication date: 3-Jun-2024
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cover image ACM Conferences
RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems
September 2021
883 pages
ISBN:9781450384582
DOI:10.1145/3460231
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2021

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RecSys '21: Fifteenth ACM Conference on Recommender Systems
September 27 - October 1, 2021
Amsterdam, Netherlands

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

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Cited By

View all
  • (2024)Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural CitizenshipACM Transactions on Recommender Systems10.1145/36431382:1(1-32)Online publication date: 7-Mar-2024
  • (2024)RADio* – An Introduction to Measuring Normative Diversity in News RecommendationsACM Transactions on Recommender Systems10.1145/36364653:1(1-29)Online publication date: 2-Aug-2024
  • (2024)Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658926(573-584)Online publication date: 3-Jun-2024
  • (2023)Personalised Recommendations for the BBC iPlayer: Initial approach and current challengesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608867(551-553)Online publication date: 14-Sep-2023

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