Computing the volume is difficult
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Random walks and an O*(n5) volume algorithm for convex bodies
Random Structures & Algorithms
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Faster random generation of linear extensions
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Preference elicitation via theory refinement
The Journal of Machine Learning Research
A hybrid approach to reasoning with partially elicited preference models
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The decision-theoretic interactive video advisor
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Knowledge-based acquisition of tradeoff preferences for negotiating agents
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Formal specification of autonomous commerce agents
Proceedings of the 2004 ACM symposium on Applied computing
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
International Journal of Human-Computer Studies
Generating and evaluating evaluative arguments
Artificial Intelligence
Evaluating product search and recommender systems for E-commerce environments
Electronic Commerce Research
Label ranking by learning pairwise preferences
Artificial Intelligence
Label Ranking in Case-Based Reasoning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Formal and Experimental Foundations of a New Rank Quality Measure
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
A Hierarchical Methodology to Specify and Simulate Complex Computational Systems
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generating and evaluating evaluative arguments
Artificial Intelligence
Nonparametric rank-based statistics and significance tests for fuzzy data
Fuzzy Sets and Systems
Predicting user preferences via similarity-based clustering
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
User preferences discovery using fuzzy models
Fuzzy Sets and Systems
Nonlinear multicriteria clustering based on multiple dissimilarity matrices
Pattern Recognition
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We study the problem of defining similarity measures on preferences from a decision-theoretic point of view. We propose a similarity measure, called probabilistic distance, that originates from the Kendall's tau function, a well-known concept in the statistical literature. We compare this measure to other existing similarity measures on preferences. The key advantage of this measure is its extensibility to accommodate partial preferences and uncertainty. We develop efficient methods to compute this measure, exactly or approximately, under all circumstances. These methods make use of recent advances in the area of Markov chain Monte Carlo simulation. We discuss two applications of the probabilistic distance: in the construction of the Decision-Theoretic Video Advisor (DIVA), and in robustness analysis of a theory refinement technique for preference elicitation.