• Computer Science
  • Published in NIPS 2011

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 sub-modular 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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