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Towards Explanations of Anti-Recommender Content in Public Radio

Published: 06 June 2019 Publication History

Abstract

Other than private broadcasters, publicly financed broadcasters have to fulfil a public service remit. Individual playouts in public radio, therefore, consist not only of recommender content but also of 'anti-recommender content" that matches public interests. Such anti-recommender content in individual playouts may be unexpected for users and may need explanation. To find out what explanations might look like in public radio, we elicit the requirements of the public service remit for an example country. Based on these requirements, we propose an approach for designing explanations of recommendations that align with the public service remit.

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Cited By

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  • (2023)Does algorithmic filtering lead to filter bubbles in online tourist information searches?Information Technology & Tourism10.1007/s40558-023-00279-426:1(183-217)Online publication date: 29-Dec-2023

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cover image ACM Conferences
UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
June 2019
455 pages
ISBN:9781450367110
DOI:10.1145/3314183
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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Published: 06 June 2019

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Author Tags

  1. broadcasting act
  2. explanatory recommender
  3. personalized media coverage
  4. personalized radio
  5. public-service remit

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UMAP'19 Adjunct Paper Acceptance Rate 30 of 122 submissions, 25%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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  • (2023)Does algorithmic filtering lead to filter bubbles in online tourist information searches?Information Technology & Tourism10.1007/s40558-023-00279-426:1(183-217)Online publication date: 29-Dec-2023

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