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2010
IEEE

Learning optimally diverse rankings over large document collections

8 years 1 months ago
Learning optimally diverse rankings over large document collections
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.
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Go
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICML
Authors Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi
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