Linear Submodular Bandits and their Application to Diversified Retrieval

@inproceedings{Yue2011LinearSB,
  title={Linear Submodular Bandits and their Application to Diversified Retrieval},
  author={Yisong Yue and Carlos Guestrin},
  booktitle={NIPS},
  year={2011}
}
Diversified retrieval and online learning are two core research areas in the design of modern information retrieval systems. In this paper, we propose the linear submodular bandits problem, which is an online learning setting for optimizing a general class of feature-rich submodular utility models for diversified retrieval. We present an algorithm, called LSBGREEDY, and prove that it efficiently converges to a near-optimal model. As a case study, we applied our approach to the setting of… CONTINUE READING
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