Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Mental Models, Explanations, Visualizations: Promoting User-Centered Qualities in Recommender Systems
Recommender systems (RSs) are powerful tools that proactively suggest a set of personalized items to users. In doing so, they aim to predict the preferences of their users, wherein they are considered to be very accurate. In addition to algorithmic precision, user-centered qualities have recently been increasingly taken into account when evaluating the success of RSs. Examples for such qualities include the transparency of an RS, the control users are able to exert over their recommendations, and the means of exploring the item space in context of recommendations. However, research on aspects focused on human-computer interaction in RSs is still at a rather early stage. The main focus of the present thesis is to study and design RSs more holistically. In this regard, the mental models that users create of RSs are explored, explanations and their impact on user-centered variables of RSs are investigated, and techniques from information visualization (InfoVis) are applied to let users scrutinize the global context of their recommendations. The results of this research and the contributions I make to the state of the art in this context are described in greater detail below. A key contribution of this thesis consists of the results of two studies that shed light on the mental models that users of RSs develop and how these models influence the users’ perception of different system qualities. A key finding of the first, qualitative study is that many mental models tend to follow a procedural structure that can be used, for instance, as a template for designing explanations to promote transparency in RSs. In the second study, which relied on a larger sample and thus allowed quantitative conclusions, this type of procedurally structured mental models was found to correlate with a high perception of system transparency and confidence in the users’ own comprehension of the inner workings of the system. Apart from that, some users seemed to humanize the RS, assigning attributes such as “social”, “organic”, and “empathic”. ...
Mental Models, Explanations, Visualizations: Promoting User-Centered Qualities in Recommender Systems
Recommender systems (RSs) are powerful tools that proactively suggest a set of personalized items to users. In doing so, they aim to predict the preferences of their users, wherein they are considered to be very accurate. In addition to algorithmic precision, user-centered qualities have recently been increasingly taken into account when evaluating the success of RSs. Examples for such qualities include the transparency of an RS, the control users are able to exert over their recommendations, and the means of exploring the item space in context of recommendations. However, research on aspects focused on human-computer interaction in RSs is still at a rather early stage. The main focus of the present thesis is to study and design RSs more holistically. In this regard, the mental models that users create of RSs are explored, explanations and their impact on user-centered variables of RSs are investigated, and techniques from information visualization (InfoVis) are applied to let users scrutinize the global context of their recommendations. The results of this research and the contributions I make to the state of the art in this context are described in greater detail below. A key contribution of this thesis consists of the results of two studies that shed light on the mental models that users of RSs develop and how these models influence the users’ perception of different system qualities. A key finding of the first, qualitative study is that many mental models tend to follow a procedural structure that can be used, for instance, as a template for designing explanations to promote transparency in RSs. In the second study, which relied on a larger sample and thus allowed quantitative conclusions, this type of procedurally structured mental models was found to correlate with a high perception of system transparency and confidence in the users’ own comprehension of the inner workings of the system. Apart from that, some users seemed to humanize the RS, assigning attributes such as “social”, “organic”, and “empathic”. ...
Mental Models, Explanations, Visualizations: Promoting User-Centered Qualities in Recommender Systems
Kunkel, Johannes (Autor:in) / Ziegler, Johannes
13.04.2023
Hochschulschrift
Elektronische Ressource
Englisch
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