• Computer Science, Mathematics
  • Published in ICML 2018

Online Learning to Rank with Features

@article{Li2018OnlineLT,
  title={Online Learning to Rank with Features},
  author={Shuai Li and Tor Lattimore and Cs. Szepesvari},
  journal={ArXiv},
  year={2018},
  volume={abs/1810.02567}
}
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We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. [...] Key Method A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm…Expand Abstract
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