Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users. However, the required exploration drives it away from successful practices in offline learning to rank, which limits OL2Rs empirical performance and practical applicability. In this work, we propose to estimate a pairwise learning to rank model online. In each round, candidate documents are partitioned and ranked according to the models confidence on the estimated pairwise rank order, and exploration is only performed on the uncertain pairs of documents, i.e., emph{divide-and-conquer}. Regret directly defined on the number of mis-ordered pairs is proven, which connects the online solutions theoretical convergence with its expected ranking performance. Comparisons against an extensive list of OL2R baselines on two public learning to rank benchmark datasets demonstrate the effectiveness of the proposed solution.
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