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Existing online learning to rank (OL2R) solutions are limited to linearmodels, which are incompetent to capture possible non-linear relations betweenqueries and documents. In this work, to unleash the power of representationlearning in OL2R, we propose to directly learn a neural ranking model fromusers' implicit feedback (e.g., clicks) collected on the fly. We focus onRankNet and LambdaRank, due to their great empirical success and wide adoptionin offline settings, and control the notorious explore-exploit trade-off basedon the convergence analysis of neural networks using neural tangent kernel.Specifically, in each round of result serving, exploration is only performed ondocument pairs where the predicted rank order between the two documents isuncertain; otherwise, the ranker's predicted order will be followed in resultranking. We prove that under standard assumptions our OL2R solution achieves agap-dependent upper regret bound of $O(\log^2(T))$, in which the regret isdefined on the total number of mis-ordered pairs over $T$ rounds. Comparisonsagainst an extensive set of state-of-the-art OL2R baselines on two publiclearning to rank benchmark datasets demonstrate the effectiveness of theproposed solution.

Learning Neural Ranking Models Online from Implicit User Feedback