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.
- 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 ScholarCross Ref
- E. Pariser, The Filter Bubble: What The Internet Is Hiding From You. Penguin UK, 2011. Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- D. Lewandowski, ?Wie Nutzer im Suchprozess gelenkt werden: Zwischen technischer Unterstützung und interessengeleiteter Darstellung," SearchStudies, 13-May-2014. .Google Scholar
- M. D. Ekstrand, D. Kluver, F. M. Harper, and J. A. Konstan, Letting Users Choose Recommender Algorithms: An Experimental Study. ACM, 2015.Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- R. Burke, 'Hybrid Recommender Systems: Survey and Experiments," User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331--370, Nov. 2002. Google ScholarDigital Library
- 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 Scholar
- 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 Scholar
- M. Alshammari, O. Nasraoui, and B. Abdollahi, 'A Semantically Aware Explainable Recommender System using Asymmetric Matrix Factorization," presented at the IC3K 2018.Google Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- F. Ricci, L. Rokach, and B. Shapira, Introduction to Recommender Systems Handbook. Springer, 2015.Google ScholarCross Ref
- 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 Scholar
Index Terms
- Towards Explanations of Anti-Recommender Content in Public Radio
Recommendations
An Approach to Explanations for Public Radio Recommendations
UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and PersonalizationExplanations are a key concept of public-service remits. For their linear program, public broadcasters regularly report on how they reflect diversity and balance in their program. For personalized public media, however, explanations must be different, ...
Compliance of Personalized Radio with Public-Service Remits
UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and PersonalizationPublic radio broadcasters do not consider personalization in their compliance with their public-service remit so far. However, personalization brings along the risk of filter bubbles, which contradicts with the ideas of the public-service remit. We shed ...
A Step Towards Empowerment and Digital Inclusion of Rural Public in India
ICEGOV '17: Proceedings of the 10th International Conference on Theory and Practice of Electronic GovernanceThe emergence of Information and Communication Technology (ICT) has provided an easy, effective, economic and efficient way to provide various government services and welfare schemes at grass root level. Indian government is also moving towards e-...
Comments