Jaron Harambam
ABSTRACT
Recommender systems (RS) are on the rise in many domains. While they offer great promises, they also raise concerns: lack of transparency, reduction of diversity, little to no user control. In this paper, we align with the normative turn in computer science which scrutinizes the ethical and societal implications of RS. We focus and elaborate on the concept of user control because that mitigates multiple problems at once. Taking the news industry as our domain, we conducted four focus groups, or moderated think-aloud sessions, with Dutch news readers (N=21) to systematically study how people evaluate different control mechanisms (at the input, process, and output phase) in a News Recommender Prototype (NRP). While these mechanisms are sometimes met with distrust about the actual control they offer, we found that an intelligible user profile (including reading history and flexible preferences settings), coupled with possibilities to influence the recommendation algorithms is highly valued, especially when these control mechanisms can be operated in relation to achieving personal goals. By bringing (future) users' perspectives to the fore, this paper contributes to a richer understanding of why and how to design for user control in recommender systems.
- Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-aware recommender systems. In Recommender systems handbook. Springer, 217--253.Google Scholar
- Svetlin Bostandjiev, John O'Donovan, and Tobias Höllerer. 2012. TasteWeights: a visual interactive hybrid recommender system. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 35--42. Google ScholarDigital Library
- Danah Boyd and Kate Crawford. 2012. Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society 15, 5 (2012), 662--679.Google ScholarCross Ref
- Jenna Burrell. 2016. How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data Society 3, 1 (2016).Google Scholar
- Bruno Cardoso, Gayane Sedrakyan, Francisco Gutierrez, Denis Parra, Peter Brusilovsky, and Katrien Verbert. 2019. IntersectionExplorer, a multi-perspective approach for exploring recommendations. International Journal of Human-Computer Studies 121 (2019), 73--92.Google ScholarCross Ref
- Pablo Castells, Neil J Hurley, and Saul Vargas. 2015. Novelty and diversity in recommender systems. In Recommender Systems Handbook. Springer, 881--918.Google Scholar
- Kathy Charmaz. 2006. Constructing grounded theory: A practical guide through qualitative analysis. Sage.Google Scholar
- John Christman. 2004. Relational autonomy, liberal individualism, and the social constitution of selves. Philosophical studies 117, 1 (2004), 143--164.Google Scholar
- Nicholas Diakopoulos. 2016. Accountability in algorithmic decision making. Commun. ACM 59, 2 (2016), 56--62. Google ScholarDigital Library
- Michael D Ekstrand, Daniel Kluver, F Maxwell Harper, and Joseph A Konstan. 2015. Letting users choose recommender algorithms: An experimental study. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 11--18. Google ScholarDigital Library
- Michael D Ekstrand and Martijn C Willemsen. 2016. Behaviorism is not enough: better recommendations through listening to users. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 221--224. Google ScholarDigital Library
- Sarah Eskens, Natali Helberger, and Judith Moeller. 2017. Challenged by news personalisation: five perspectives on the right to receive information. Journal of Media Law 9, 2 (2017), 259--284.Google ScholarCross Ref
- Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management science 55, 5 (2009), 697--712. Google ScholarDigital Library
- Mario Haim, Andreas Graefe, and Hans-Bernd Brosius. 2018. Burst of the filter bubble? Effects of personalization on the diversity of Google News. Digital journalism 6, 3 (2018), 330--343.Google Scholar
- Jaron Harambam, Natali Helberger, and Joris van Hoboken. 2018. Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, 2133 (2018), 20180088.Google ScholarCross Ref
- F Maxwell Harper, Funing Xu, Harmanpreet Kaur, Kyle Condiff, Shuo Chang, and Loren Terveen. 2015. Putting users in control of their recommendations. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 3--10. Google ScholarDigital Library
- Chen He, Denis Parra, and Katrien Verbert. 2016. Interactive recommender systems: A survey of the sota and future research challenges and opportunities. Expert Systems With Applications 56 (2016), 9--27. Google ScholarDigital Library
- Natali Helberger. 2019. On the democratic role of news recommenders. Digital Journalism (2019), 1--20.Google Scholar
- Natali Helberger, Kari Karpinnen, and Lucia D'Acunto. 2018. Exposure diversity as a design principle for recommender systems. Information, Communication Society 21, 2 (2018), 191--207.Google ScholarCross Ref
- Anthony Jameson, Martijn C Willemsen, Alexander Felfernig, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, and Li Chen. 2015. Human decision making and recommender systems. In Recommender Systems Handbook. Springer, 611--648.Google Scholar
- Dietmar Jannach, Sidra Naveed, and Michael Jugovac. 2017. User control in recommender systems: Overview and interaction challenges. In Proceedings of 17th International Conference on E-Commerce and Web Technologies 2016. Springer, Cham, SH, 21--33.Google ScholarCross Ref
- Lanier Jaron. 2010. You Are Not a Gadget: A Manifesto. New York: Knopf (2010). Google ScholarDigital Library
- Yucheng Jin, Nava Tintarev, and Katrien Verbert. 2018. Effects of personal characteristics on music recommender systems with different levels of controllability. In 18 Proceedings of the 12th ACM Conference on Recommender Systems. ACM Press, New York, NA, US. RecSys, 13--21. Google ScholarDigital Library
- Michael Jugovac and Dietmar Jannach. 2017. Interacting with recommenders - overview and research directions. ACM Transactions on Interactive Intelligent Systems (TiiS) 7, 3 (2017), 1--10. Google ScholarDigital Library
- Marius Kaminskas and Derek Bridge. 2017. Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 7, 1 (2017), 2. Google ScholarDigital Library
- Mozhgan Karimi, Dietmar Jannach, and Michael Jugovac. 2018. News recommender systems-Survey and roads ahead. Information Processing & Management 54, 6 (2018), 1203--1227.Google ScholarCross Ref
- Rob Kitchin. 2017. Thinking critically about and researching algorithms. Information, Communication Society 20, 1 (2017), 14--29.Google ScholarCross Ref
- Bart Knijnenburg, Niels Reijmer, and Martijn Willemsen. 2011. Each to his own: How different users call for different interaction methods in recommender systems. In 11 Proceedings of the fifth ACM conference on Recommender systems. ACM Press, New York, NA, US. RecSys, 141--148. Google ScholarDigital Library
- Bart P Knijnenburg, Saadhika Sivakumar, and Daricia Wilkinson. 2016. Recommender systems for self-actualization. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 11--14. Google ScholarDigital Library
- Joseph Konstan and John Riedl. 2012. Recommender systems: From algorithms to user experience. User modeling and user-adapted interaction 22, 1-2 (2012), 101--123. Google ScholarDigital Library
- Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. 2016. A survey of serendipity in recommender systems. Knowledge-Based Systems 111 (2016), 180--192. Google ScholarDigital Library
- Sara Leckner. 2012. Presentation factors affecting reading behaviour in readers of newspaper media: an eye-tracking perspective. Visual Communication 11, 2 (2012), 163--184.Google ScholarCross Ref
- Jin Ha Lee and Rachel Price. {n.d.}. Understanding users of commercial music services through personas: Design implications. In Proceedings of 16th International Society for Music Information Retrieval Conference. ACM Press, New York, NA, US, 476--482.Google Scholar
- Sean McNee, John Riedl, and Joseph Konstan. 2006. Making recommendations better: An analytic model for Human-Recommender Interaction. In Abstracts of the 2006 ACM Conference on Human Factors in Computing Systems. ACM Press, New York, NY, US, 1003--1008. Google ScholarDigital Library
- David L Morgan. 1996. Focus groups as qualitative research. Vol. 16. Sage publications.Google Scholar
- Cathy O'Neil. 2017. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books. Google Scholar
- Eli Pariser. 2011. The filter bubble: What the Internet is hiding from you. Penguin, London, UK. Google ScholarDigital Library
- Denis Parra and Peter Brusilovsky. 2015. User-controllable personalization: A case study with SetFusion. International Journal of Human-Computer Studies 78 (2015), 43--67. Google ScholarDigital Library
- Eric A Posner and E Glen Weyl. 2018. Radical markets: Uprooting capitalism and democracy for a just society. Princeton University Press.Google Scholar
- Pearl Pu, Li Chen, and Rong Hu. 2012. Evaluating recommender systems from the userâĂŹs perspective: survey of the state of the art. User Modeling and User-Adapted Interaction 22, 4-5 (2012), 317--355. Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender systems handbook. Springer, Cham, SH. Google Scholar
- James Schaffer, Tobias Hollerer, and John O'Donovan. 2015. Hypothetical recommendation: A study of interactive profile manipulation behavior for recommender systems. In The Twenty-Eighth International Flairs Conference.Google Scholar
- Douglas Schuler and Aki Namioka. 1993. Participatory design: Principles and practices. CRC Press. Google ScholarDigital Library
- David Silverman. 2016. Qualitative research. Sage.Google Scholar
- Rashmi Sinha and Kirsten Swearingen. 2002. The role of transparency in recommender systems. In CHI'02 extended abstracts on Human factors in computing systems. ACM, 830--831. Google ScholarDigital Library
- Emily Sullivan, Dimitrios Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, Mykola Makhortykh, Domokos Kelen, Daricia Wilkinson, David Graus, and Nava Tintarev. {n.d.}. Reading News with a Purpose: Explaining User Profiles for Self-Actualization. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. ACM, 241--245. Google ScholarDigital Library
- Nava Tintarev and Judith Masthoff. 2015. Explaining recommendations: Design and evaluation. In Recommender Systems Handbook, Lior Rokach and Bracha Shapira (Eds.). Springer, Cham, SH, 353--382.Google Scholar
- Max van Kleek Ulrik Lyngs, Reuben Binns and Nigel Shadbolt. 2018. So, tell me what users want, what they really, really want!. In CHI EA '18 (alt.chi) Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. ACM Press, New York, NY, US, 1--10. Google ScholarDigital Library
- Malte Ziewitz. 2016. Governing algorithms: Myth, mess, and methods. Science, Technology, & Human Values 41, 1 (2016), 3--16.Google ScholarCross Ref
Index Terms
- Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems
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