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Towards Explanations of Anti-Recommender Content in Public Radio

Published:06 June 2019Publication 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.

References

  1. N. Pöchhacker, M. Burkhardt, A. Geipel, and J.-H. Passoth, 'Interventionen in die Produktion algorithmischer Öffentlichkeiten: Recommender Systeme als Herausforderung für öffentlich-rechtliche Sendeanstalten.," kommunikation @ gesellschaft, vol. 18, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  2. E. Pariser, The Filter Bubble: What The Internet Is Hiding From You. Penguin UK, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. F. Zuiderveen Borgesius et al., 'Should We Worry About Filter Bubbles?," Social Science Research Network, Rochester, NY, SSRN Scholarly Paper ID 2758126, Apr. 2016.Google ScholarGoogle Scholar
  4. J. A. Konstan and J. Riedl, ?Recommender systems: from algorithms to user experience," User Model User-Adap Inter, vol. 22, no. 1--2, pp. 101--123, Apr. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Lewandowski, ?Wie Nutzer im Suchprozess gelenkt werden: Zwischen technischer Unterstützung und interessengeleiteter Darstellung," SearchStudies, 13-May-2014. .Google ScholarGoogle Scholar
  6. M. D. Ekstrand, D. Kluver, F. M. Harper, and J. A. Konstan, Letting Users Choose Recommender Algorithms: An Experimental Study. ACM, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. L. Iaquinta, M. d Gemmis, P. Lops, G. Semeraro, M. Filannino, and P. Molino, 'Introducing Serendipity in a Content-Based Recommender System," in 2008 International Conference on Hybrid Intelligent Systems, 2008, pp. 168--173.Google ScholarGoogle Scholar
  8. L. Li, D.-D. Wang, S.-Z. Zhu, and T. Li, 'Personalized News Recommendation: A Review and an Experimental Investigation," Journal of Computer Science and Technology, vol. 26, no. 5, pp. 754--766, Sep. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. R. Burke, 'Hybrid Recommender Systems: Survey and Experiments," User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331--370, Nov. 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Z. Brand, 'NPR Digital Media: lessons learned in creating and delivering a digital listening experience," presented at the Radio 2.0 Keynote, Paris, 2015.Google ScholarGoogle Scholar
  11. X. Wang, Y. Chen, J. Yang, L. Wu, Z. Wu, and X. Xie, 'A Reinforcement Learning Framework for Explainable Recommendation," in 2018 IEEE International Conference on Data Mining (ICDM), 2018, pp. 587--596.Google ScholarGoogle Scholar
  12. M. Alshammari, O. Nasraoui, and B. Abdollahi, 'A Semantically Aware Explainable Recommender System using Asymmetric Matrix Factorization," presented at the IC3K 2018.Google ScholarGoogle Scholar
  13. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, 'A Survey of Methods for Explaining Black Box Models," ACM Comput. Surv., vol. 51, no. 5, pp. 93:1--93:42, Aug. 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. B. Abdollahi and O. Nasraoui, 'Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems," in Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, J. Zhou and F. Chen, Eds. Cham: Springer International Publishing, 2018, pp. 21--35.Google ScholarGoogle Scholar
  15. R. Sinha and K. Swearingen, 'The Role of Transparency in Recommender Systems," in CHI '02 Extended Abstracts on Human Factors in Computing Systems, New York, NY, USA, 2002, pp. 830--831. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. F. Ricci, L. Rokach, and B. Shapira, Introduction to Recommender Systems Handbook. Springer, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  17. M. Schreier, 'Qualitative Content Analysis," in The SAGE Handbook of Qualitative Data Analysis, U. Flick, Ed. London, UK: SAGE Publications Ltd, 2014, pp. 170--183.Google ScholarGoogle Scholar

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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

        Copyright © 2019 ACM

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        Publication History

        • Published: 6 June 2019

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        UMAP'19 Adjunct Paper Acceptance Rate30of122submissions,25%Overall Acceptance Rate162of633submissions,26%

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