GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Towards more conversational and collaborative recommender systems
Proceedings of the 8th international conference on Intelligent user interfaces
Personal choice point: helping users visualize what it means to buy a BMW
Proceedings of the 8th international conference on Intelligent user interfaces
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Stereotypes, Student Models and Scrutability
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Goal-Based Construction of Preferences: Task Goals and the Prominence Effect
Management Science
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Designing example-critiquing interaction
Proceedings of the 9th international conference on Intelligent user interfaces
ValueCharts: analyzing linear models expressing preferences and evaluations
Proceedings of the working conference on Advanced visual interfaces
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence and Law - Online dispute resolution
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion
UM '07 Proceedings of the 11th international conference on User Modeling
Proceedings of the 2008 ACM conference on Recommender systems
ACM Conference on Recommender Systems
Interaction design guidelines on critiquing-based recommender systems
User Modeling and User-Adapted Interaction
Multi-angle view on preference elicitation for negotiation support systems
HuCom '08 Proceedings of the 1st International Working Conference on Human Factors and Computational Models in Negotiation
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Content-based recommendation systems
The adaptive web
Real time labeling of affect in music using the affectbutton
Proceedings of the 3rd international workshop on Affective interaction in natural environments
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
User-centered design of preference elicitation interfaces for decision support
USAB'10 Proceedings of the 6th international conference on HCI in work and learning, life and leisure: workgroup human-computer interaction and usability engineering
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Evaluating the effectiveness of explanations for recommender systems
User Modeling and User-Adapted Interaction
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
Towards value-focused decision support systems
Proceedings of the 30th European Conference on Cognitive Ergonomics
AffectButton: A method for reliable and valid affective self-report
International Journal of Human-Computer Studies
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Two problems may arise when an intelligent (recommender) system elicits users' preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users' mental preference models. Giving exact numbers, e.g., such as "I like 30 days of vacation 2.5 times better than 28 days" is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.