Reaching the End of Unbiasedness
Harrie Oosterhuis
Abstract
Click-based learning to rank (LTR) tackles the mismatch between click frequencies on items and their actual relevance. The approach of previous work has been to assume a model of click behavior and to subsequently introduce a method for unbiasedly estimating preferences under that assumed model. The success of this approach is evident in that unbiased methods have been found for an increasing number of behavior models and types of bias.
Topics & Concepts
Relevance (law)Computer scienceRank (graph theory)Artificial intelligenceMachine learningEconometricsMathematicsCombinatoricsLawPolitical scienceInformation Retrieval and Search BehaviorConsumer Market Behavior and PricingMulti-Criteria Decision Making