This monograph takes a step towards promoting the study of efficiency in the era of neural information retrieval by offering a comprehensive survey of the literature on efficiency and effectiveness in ranking, and to a limited extent, retrieval. This monograph was inspired by the parallels that exist between the challenges in neural network-based ranking solutions and their predecessors, decision forest-based learning to rank models, as well as the connections between the solutions the literature to date has to offer. We believe that by understanding the fundamentals underpinning these algorithmic and data structure solutions for containing the contentious relationship between efficiency and effectiveness, one can better identify future directions and more efficiently determine the merits of ideas. We also present what we believe to be important research directions in the forefront of efficiency and effectiveness in retrieval and ranking.

翻译：本专著旨在推动神经信息检索时代对效率问题的研究，通过全面综述排序（以及在一定程度上检索）领域关于效率与效果的文献。本专著的灵感来源于基于神经网络的排序解决方案与其前身——基于决策森林的学习排序模型在挑战上的相似性，以及现有文献中解决方案之间的关联。我们认为，通过理解制约效率与效果之间矛盾关系的算法和数据结构解决方案的基本原理，研究者能够更好地识别未来方向，并更高效地判断各种想法的优劣。此外，我们还提出了在检索与排序领域中处于效率与效果研究前沿的重要研究方向。