On the complexity of computing the volume of a polyhedron
SIAM Journal on Computing
A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
Random walks and an O*(n5) volume algorithm for convex bodies
Random Structures & Algorithms
Design and implementation of an agent-based intermediary infrastructure for electronic markets
Proceedings of the 2nd ACM conference on Electronic commerce
Double Description Method Revisited
Selected papers from the 8th Franco-Japanese and 4th Franco-Chinese Conference on Combinatorics and Computer Science
Analytic Centers and Repelling Inequalities
Analytic Centers and Repelling Inequalities
Winner Determination Algorithms for Electronic Auctions: A Framework Design
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Polyhedral sampling for multiattribute preference elicitation
Proceedings of the 4th ACM conference on Electronic commerce
Research challenges of autonomic computing
Proceedings of the 27th international conference on Software engineering
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Achieving Self-Management via Utility Functions
IEEE Internet Computing
Regret-based utility elicitation in constraint-based decision problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Applications of flexible pricing in business-to-business electronic commerce
IBM Systems Journal
A research agenda for business-driven information technology
HotACI'06 Proceedings of the First international conference on Hot topics in autonomic computing
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The task of evaluating and ranking items with multiple-attributes appears in many guises in commerce. Examples include evaluating responses to a request for quotes (RFQ) for some item and comparison shopping for an item within one or more catalogs. This task is straightforward if the value of the item can be explicitly specified by the evaluator as a function of the attribute values. However, a typical evaluator may not be able to provide the value function in explicit form. In contrast, it is intuitive for them to compare, say, two items and pick the preferable one based on all of the relevant attributes. In this paper we present a method, Q-Eval, that queries the evaluator with selected pairs of items and uses the responses to build a preference model for the evaluator. This model is then used to rank the items in order of the inferred preference. The evaluator can then pick the winning item or items by considering only the top few items in this ranked list. This should result in significant productivity improvement for the evaluator when the number of items to choose from is large. Our algorithm is novel in the way it attempts to derive a stable preference model with only a small number of user queries. This paper describes the algorithm and presents experimental results with real-life data to validate the approach.