Interactively optimizing information retrieval systems as a dueling bandits problem

Yisong Yue · Thorsten Joachims

268 citations0/0 files storedInternational Conference on Machine Learning

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

Abstract

We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, we only require pairwise comparisons which were shown to be reliably inferred from implicit feedback (Joachims et al., 2007; Radlinski et al., 2008b). We will present an algorithm with theoretical guarantees as well as simulation results.

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References

2003Online convex programming and generalized infinitesimal gradient ascent 2004Online convex optimization in the bandit setting: gradient descent without a gradient 2008Learning diverse rankings with multi-armed bandits 2007The Epoch-Greedy algorithm for contextual multi-armed bandits 2004Nearly Tight Bounds for the Continuum-Armed Bandit Problem

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2012The K-armed dueling bandits problem 2014Reducing Dueling Bandits to Cardinal Bandits 2016Contextual Bandits in a Collaborative Environment 2018Learning Contextual Bandits in a Non-stationary Environment 2017Online Learning to Rank in Stochastic Click Models 2015Coactive Learning 2013Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem 2013Distributed Exploration in Multi-Armed Bandits

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