• Publications
  • Influence
Explaining the user experience of recommender systems
TLDR
This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Expand
Inspectability and control in social recommenders
TLDR
An online user experiment with a Facebook music recommender system that gives users control over the recommendations is performed, and the results show that inspectability and control indeed increase users' perceived understanding of and control of the system, their rating of the recommendation quality, and their satisfaction with the system. Expand
Each to his own: how different users call for different interaction methods in recommender systems
TLDR
The results show that most users (and particularly domain experts) are most satisfied with a hybrid recommender that combines implicit and explicit preference elicitation, but that novices and maximizers seem to benefit more from a non-personalizedRecommender that just displays the most popular items. Expand
Understanding choice overload in recommender systems
TLDR
Investigation of the effect of recommendation set size and set quality on perceived variety, recommendation set attractiveness, choice difficulty and satisfaction with the chosen item shows that larger sets containing only good items do not necessarily result in higher choice satisfaction compared to smaller sets. Expand
Privacy Aspects of Recommender Systems
TLDR
It is concluded that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users. Expand
A pragmatic procedure to support the user-centric evaluation of recommender systems
TLDR
This work introduces a pragmatic procedure to evaluate recommender systems for experience products with test users, within industry constraints on time and budget. Expand
Evaluating Recommender Systems with User Experiments
TLDR
This chapter provides a detailed practical description of how to conduct user experiments, covering the following topics: formulating hypotheses, sampling participants, creating experimental manipulations, measuring subjective constructs with questionnaires, and statistically evaluating the results. Expand
Making privacy personal: Profiling social network users to inform privacy education and nudging
TLDR
It is shown that Facebook users' privacy behaviors and awareness are multi-dimensional and can be used to personalize user education and nudging. Expand
Inferring Capabilities of Intelligent Agents from Their External Traits
TLDR
It is demonstrated that the mental model users form of an agent-based system is inherently integrated (as opposed to the compositional mental model they form of conventional interfaces): Cues provided by the system do not instill user responses in a one-to-one matter but are instead integrated into a single mental model. Expand
Effectiveness and Users' Experience of Obfuscation as a Privacy-Enhancing Technology for Sharing Photos
TLDR
It is suggested inpainting and avatar may be useful as privacy-enhancing technologies for photos, because they are both effective at increasing privacy for elements of a photo and provide a good viewer experience. Expand
...
1
2
3
4
5
...