Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evaluating compound critiquing recommenders: a real-user study
Proceedings of the 8th ACM conference on Electronic commerce
Eliciting bid taker non-price preferences in (combinatorial) auctions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
Mechanism design with partial revelation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incremental utility elicitation with minimax regret decision criterion
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Robust solutions of uncertain linear programs
Operations Research Letters
Optimal social choice functions: a utilitarian view
Proceedings of the 13th ACM Conference on Electronic Commerce
Robust approximation and incremental elicitation in voting protocols
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Multi-winner social choice with incomplete preferences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Hi-index | 0.00 |
Product recommendation and decision support systems must generally develop a model of user preferences by querying or otherwise interacting with a user. Recent approaches to elicitation using minimax regret have proven to be very powerful in simulation. In this work, we test both the effectiveness of regret-based elicitation, and user comprehension and acceptance of minimax regret in user studies. We report on a study involving 40 users interacting with the UTPref Recommendation System, which helps students navigate and find rental accommodation. UTPref maintains an explicit (but incomplete) generalized additive utility (GAI) model of user preferences, and uses minimax regret for recommendation. We assess the following general questions: How effective is regret-based elicitation in finding optimal or near-optimal products? Do users understand and accept the minimax regret criterion in practice? Do decision-theoretically valid queries for GAI models result in more accurate assessment than simpler, ad hoc queries? On the first two issues, we find that the minimax regret decision criterion is effective, understandable, and intuitively appealing. On the third issue, we find that simple, semantically ambiguous query types perform as well as more demanding, semantically valid queries for GAI models. We also assess the relative difficulty of specific query types.