Neural information retrieval: introduction to the special issue

Abstract

The applications of neural network models, shallow or deep, to information retrieval (IR) tasks falls under the purview of neural IR. Over the years, machine learning methods-including neural networks-have been popularly employed in IR, such as in learning-to-rank (LTR) frameworks. Recently, neural representation learning and neural models with deep architectures have demonstrated significant improvements in speech recognition, machine translation, and computer vision tasks. Similar methods are now being explored by the IR community that may lead to new models and performance breakthroughs for retrieval scenarios. This special issue of the Information Retrieval journal provides an additional venue for the findings from research happening at the intersection of information retrieval and neural networks.