  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.
