Marketing Science
Experiments on the preference-based organization interface in recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Hidden markets: UI design for a P2P backup application
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Market design & analysis for a P2P backup system
Proceedings of the 11th ACM conference on Electronic commerce
Automatically generating personalized user interfaces with Supple
Artificial Intelligence
The good, the bad, and the random: an eye-tracking study of ad quality in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Individual differences in gaze patterns for web search
Proceedings of the third symposium on Information interaction in context
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Journal of Artificial Intelligence Research
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Despite the pervasiveness of markets in our lives, little is known about the role of user interfaces (UIs) in promoting good decisions in market domains. How does the way we display market information to end users, and the set of choices we offer, influence users' decisions? In this paper, we introduce a new research agenda on "market user interface design." Our goal is to find the optimal market UI, taking into account that users incur cognitive costs and are boundedly rational. Via lab experiments we systematically explore the market UI design space, and we study the automatic optimization of market UIs given a behavioral (quantal response) model of user behavior. Surprisingly, we find that the behaviorally-optimized UI performs worse than the standard UI, suggesting that the quantal response model did not predict user behavior well. Subsequently, we identify important behavioral factors that are missing from the user model, including loss aversion and position effects, which motivates follow-up studies. Furthermore, we find significant differences between individual users in terms of rationality. This suggests future research on personalized UI designs, with interfaces that are tailored towards each individual user's needs, capabilities, and preferences.