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Online Learning to Rank in Stochastic Click Models
Masrour Zoghi · Tomas Tunys · Mohammad Ghavamzadeh · Branislav Kveton · Csaba Szepesvari · Zheng Wen

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #30

Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts with a list of documents. Though these results are significant, their impact on practice is limited, because all proposed algorithms are designed for specific click models and lack convergence guarantees in other models. In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models. The class encompasses two most fundamental click models, the cascade and position-based models. We derive a gap-dependent upper bound on the T-step regret of BatchRank and evaluate it on a range of web search queries. We observe that BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an existing algorithm for the cascade model.

Author Information

Masrour Zoghi (Independent Researcher)
Tomas Tunys (Czech Technical University)
Mohammad Ghavamzadeh (Adobe Research & INRIA)
Branislav Kveton (Adobe Research)
Csaba Szepesvari (University of Alberta)
Zheng Wen (Adobe Research)

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