Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems

Harambam, Jaron; Bountouridis, Dimitrios; Makhortykh, Mykola; Van Hoboken, Joris (2019). Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems. In: RecSys 2019. Proceedings of the 13th ACM Conference on Recommender Systems (pp. 69-77). New York: ACM 10.1145/3298689.3347014

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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.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Mass Communication Studies

UniBE Contributor:

Makhortykh, Mykola

Subjects:

000 Computer science, knowledge & systems
000 Computer science, knowledge & systems > 070 News media, journalism & publishing
300 Social sciences, sociology & anthropology

ISBN:

978-1-4503-6243-6

Series:

Proceedings of the 13th ACM Conference on Recommender Systems

Publisher:

ACM

Language:

English

Submitter:

Mykola Makhortykh

Date Deposited:

30 Sep 2019 17:12

Last Modified:

22 Oct 2019 20:04

Publisher DOI:

10.1145/3298689.3347014

Uncontrolled Keywords:

Recommender Systems, Personalization, User Interfaces, User-Centered Design, Human factors, Interaction paradigms.

BORIS DOI:

10.7892/boris.133567

URI:

https://boris.unibe.ch/id/eprint/133567

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