Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback
Yiling Jia, Hongning Wang · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
Deep neural networks (DNNs) demonstrates significant advantages in improving ranking performance in retrieval tasks. Driven by the recent 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.