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Linear Submodular Bandits and their Application to Diversified Retrieval

Yue, Yisong and Guestrin, Carlos (2011) Linear Submodular Bandits and their Application to Diversified Retrieval. In: Advances in Neural Information Processing Systems 24. Advances in Neural Information Processing Systems. No.24. Neural Information Processing Systems , Red Hook, NY, pp. 1-9. ISBN 9781618395993.

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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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Yue, Yisong0000-0001-9127-1989
Additional Information:© 2011 Neural Information Processing Systems Foundation, Inc. This work was funded in part by ONR (PECASE) N000141010672 and ONR Young Investigator Program N00014-08-1-0752. The authors also thank Khalid El-Arini, Joey Gonzalez, Sue Ann Hong, Jing Xiang, and the anonymous reviewers for their helpful comments.
Funding AgencyGrant Number
Office of Naval Research (ONR)N000141010672
Office of Naval Research (ONR)N00014-08-1-0752
Series Name:Advances in Neural Information Processing Systems
Issue or Number:24
Record Number:CaltechAUTHORS:20190416-081616693
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94727
Deposited By: Tony Diaz
Deposited On:16 Apr 2019 23:29
Last Modified:03 Oct 2019 21:06

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