Learning optimally diverse rankings over large document collections

Details Learning optimally diverse rankings over large document collections Learning optimally diverse rankings over large document collections

2010-05-28

arxiv.org/abs/1005.5197v1

Computer Science

Learning

This is the full version of a paper in ICML'10

Scientific paper

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-to-rank 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.

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