  Deep neural networks (DNNs) demonstrate significant advantages in improving
ranking performance in retrieval tasks. Driven by the recent technical
developments in optimization and generalization of DNNs, learning a neural
ranking model online from its interactions with users becomes possible.
However, the required exploration for model learning has to be performed in the
entire neural network parameter space, which is prohibitively expensive and
limits the application of such online solutions in practice.
  In this work, we propose an efficient exploration strategy for online
interactive neural ranker learning based on the idea of bootstrapping. Our
solution employs an ensemble of ranking models trained with perturbed user
click feedback. The proposed method eliminates explicit confidence set
construction and the associated computational overhead, which enables the
online neural rankers' training to be efficiently executed in practice with
theoretical guarantees. Extensive comparisons with an array of state-of-the-art
OL2R algorithms on two public learning to rank benchmark datasets demonstrate
the effectiveness and computational efficiency of our proposed neural OL2R
solution.
