Linear Submodular Bandits and their Application to Diversified Retrieval

Yisong Yue · Carlos Guestrin

101 citations0/0 files storedCaltechAUTHORS (California Institute of Technology)

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Advanced Bandit Algorithms Research Information Retrieval and Search Behavior Recommender Systems and Techniques

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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References

2011Contextual Bandits with Linear Payoff Functions 2008Stochastic Linear Optimization Under Bandit Feedback 2008Learning diverse rankings with multi-armed bandits 2010Linearly Parameterized Bandits 2007The Epoch-Greedy algorithm for contextual multi-armed bandits 2008Competing in the dark: An efficient algorithm for bandit linear optimization

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