Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows

Busa-Fekete, Róbert; Hüllermeier, Eyke und Szörényi, Balázs (2014): Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows. 31st International Conference on Machine Learning (ICML 2014), Beijing, China, 21-26 June, 2014. Xing, Eric P. und Jebara, Tony (eds.) : In: Proceedings of the 31st International Conference on Machine Learning, Vol. 32, No. 2 Bejing, China: PMLR. pp. 1071-1079

Abstract

We address the problem of rank elicitation assuming that the underlying data generating process is characterized by a probability distribution on the set of all rankings (total orders) of a given set of items. Instead of asking for complete rankings, however, our learner is only allowed to query pairwise preferences. Using information of that kind, the goal of the learner is to reliably predict properties of the distribution, such as the most probable top-item, the most probable ranking, or the distribution itself. More specifically, learning is done in an online manner, and the goal is to minimize sample complexity while guaranteeing a certain level of confidence.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• Place of Publication: Bejing, China