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Linear Submodular Bandits and their Application to Diversified Retrieval (2011)

By: Yisong Yue and Carlos Guestrin

Abstract: 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 personalized news recommendation, where the system must recommend small sets of news articles selected from tens of thousands of available articles each day. In a live user study, we found that LSBGREEDY significantly outperforms existing online learning approaches.

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Yisong Yue and Carlos Guestrin (2011). "Linear Submodular Bandits and their Application to Diversified Retrieval." In Advances in Neural Information Processing Systems (NIPS). pdf long   poster    
BibTeX citation

@inproceedings{yue-guestrin:nips2011,
Author = {Yisong Yue and Carlos Guestrin},
booktitle = {In Advances in Neural Information Processing Systems (NIPS)},
title = {Linear Submodular Bandits and their Application to Diversified Retrieval},
year = {2011},
address = {Granada, Spain},
month = {December},
wwwfilebase = {nips2011-yue-guestrin},
wwwtopic = {Online Learning},
}



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