Electronic Commerce Research
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Evaluating example-based search tools
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Integrating tradeoff support in product search tools for e-commerce sites
Proceedings of the 6th ACM conference on Electronic commerce
Conversational recommenders with adaptive suggestions
Proceedings of the 2007 ACM conference on Recommender systems
Research Note: User Design of Customized Products
Marketing Science
Marketing Science
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
The adaptive web
The effect of preference elicitation methods on the user experience of a recommender system
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Understanding choice overload in recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Each to his own: how different users call for different interaction methods in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
A pragmatic procedure to support the user-centric evaluation of recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Conducting user experiments in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Understanding buyers' social information needs during purchase decision process
Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
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In a recommender system that suggests options based on user attribute weights, the method of preference elicitation (PE) employed by a recommender system can influence users' satisfaction with the system, as well as the perceived usefulness and the understandability of the system. Specifically, we hypothesize that users with different levels of domain knowledge prefer different types of PE. While domain experts reported higher satisfaction and perceived usefulness with attribute-based PE (i.e., indicating preference levels for the domain-related attributes), novices preferred case-based PE (i.e., indicating the preference for specific examples, from which attribute-preferences can then be implicitly calculated). The paper discusses the decision-theoretical principles that are believed to lead to this distinction, as well as an experiment that provides substantial evidence for the hypothesis. Consequently, we introduce the idea of adapting the method of PE to users' domain knowledge on the fly using click stream data.