Feature-Oriented vs. Needs-Oriented Product Access for Non-Expert-Online Shoppers
I3E '01 Proceedings of the IFIP Conference on Towards The E-Society: E-Commerce, E-Business, E-Government
Well-integrated needs-oriented recommender components regarded as helpful
CHI '04 Extended Abstracts on Human Factors in Computing Systems
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
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Conversational recommenders with adaptive suggestions
Proceedings of the 2007 ACM conference on Recommender systems
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
Proceedings of the third 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
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To increase the user experience, preference elicitation methods used by recommender systems can be adapted to individual differences such as the level of expertise. However, we will show that the satisfaction and perceived usefulness of a recommender system also depends strongly on subtle variations of the implementation of these methods.