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Learning optimally diverse rankings over large document collections
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@MISC{Slivkins_learningoptimally,
author = {Aleksandrs Slivkins and Filip Radlinski and Sreenivas Gollapudi},
title = {Learning optimally diverse rankings over large document collections},
year = {}
}
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Abstract
Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-torank formulation that optimizes the fraction of satisfied users, with a scalable algorithm that explicitly takes document similarity and ranking context into account. We present theoretical justifications for this approach, as well as a near-optimal algorithm. Our evaluation adds optimizations that improve empirical performance, and shows that our algorithms learn orders of magnitude more quickly than previous approaches. 1.
Keyphrases
large document collection diverse ranking present theoretical justification empirical performance learned ranking function algorithm learn order document similarity different document redundant result satisfied user scalable algorithm learning-torank formulation lacked theoretical foundation previous approach near-optimal algorithm
