Exploring Mental Models for Transparent and Controllable Recommender Systems: A Qualitative Study
Ngo, T. P., Kunkel, J., & Ziegler, J. (2020). UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 183–191.
Abstract
While online content is personalized to an increasing degree, eg. using recommender systems (RS), the rationale behind personalization and how users can adjust it typically remains opaque. This was often observed to have negative effects on the user experience and perceived quality of RS. As a result, research increasingly has taken user-centric aspects such as transparency and control of a RS into account, when assessing its quality. However, we argue that too little of this research has investigated the users’ perception and understanding of RS in their entirety. In this paper, we explore the users’ mental models of RS. More specifically, we followed the qualitative grounded theory methodology and conducted 10 semi-structured face-to-face interviews with typical and regular Netflix users. During interviews participants expressed high levels of uncertainty and confusion about the RS in Netflix. Consequently, we found a broad range of different mental models. Nevertheless, we also identified a general structure underlying all of these models, consisting of four steps: data acquisition, inference of user profile, comparison of user profiles or items, and generation of recommendations. Based on our findings, we discuss implications to design more transparent, controllable, and user friendly RS in the future.
