LEARNING DIVERSE RANKINGS OVER DOCUMENT COLLECTIONS

摘要

A document selector selects and ranks documents that are relevant to a query. The document selector executes an instance of a multi-armed bandits algorithm to select a document for each slot of a results page according to one or more strategies. The documents are selected in an order defined by the results page and documents selected for previous slots are used to guide the selection of a document for a current slot. If a document in a slot is subsequently selected, the strategy used to select the document is rewarded with positive feedback. When the uncertainty in an estimate of the utility of a strategy is less than the variation between documents associated with the strategy, the strategy is subdivided into multiple strategies. The document selector is able to “zoom in” on effective strategies and provide more relevant search results.