Linear Submodular Bandits and their Application to Diversified Retrieval
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.
© 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.
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