论文标题
神经在线学习的可扩展探索，以扰动反馈排名
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback
论文作者
论文摘要
深度神经网络（DNNS）在改善检索任务中的排名绩效方面具有显着优势。在DNN的优化和概括方面的最新技术发展的驱动下，可以从与用户的交互中学习神经排名模型。但是，必须在整个神经网络参数空间中进行模型学习所需的探索，该空间非常昂贵，并限制了这种在线解决方案在实践中的应用。 在这项工作中，我们根据引导的想法为在线交互式神经排名学习提出了有效的探索策略。我们的解决方案采用了通过扰动用户点击反馈训练的排名模型集合。所提出的方法消除了明确的置信度集合和相关的计算开销，这使在线神经排名者的培训能够通过理论保证在实践中有效执行。在两种公共学习中，与一系列最新的OL2R算法进行了广泛的比较，以对基准数据集进行排名，这证明了我们提出的神经OL2R解决方案的有效性和计算效率。
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.
