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

Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi

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…