Ranked bandits in metric spaces: learning diverse rankings over large document collections

@article{Slivkins2013RankedBI,
  title={Ranked bandits in metric spaces: learning diverse rankings over large document collections},
  author={Aleksandrs Slivkins and Filip Radlinski and Sreenivas Gollapudi},
  journal={Journal of Machine Learning Research},
  year={2013},
  volume={14},
  pages={399-436}
}
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 several scalable algorithms that explicitly takes document similarity and ranking context into account. Our formulation… CONTINUE READING
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