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.

Sebastian Bruch

Claudio Lucchese

Franco Maria Nardini

Foundations and Trends® in Information Retrieval

FOUNDATIONS AND TRENDS® IN INFORMATION RETRIEVAL 发表关于以下主题的调查和教程文章：IRARCHITECTURES FOR IR 协作过滤和推荐系统的应用跨语言和多语言 IR 分布式 IR 和联合搜索评估问题和测试集合 IRFORMAL 模型和移动平台上 IRIR 的语言模型索引结构化文档和检索信息分类和聚类信息提取信息过滤和路由元搜索、排名聚合和数据融合自然语言处理 IR 系统的性能问题，包括算法、数据结构、优化技术和可扩展性问答单文档、多文档和语料库的摘要文本挖掘主题检测IRWEB 搜索的 IRUSER 建模和用户研究中的可用性、交互性和可视化问题。

Foundations and Trends in Information Retrieval publishes survey and tutorial articles on topics in the field of information retrieval, including: architectures for information retrieval, applications of collaborative filtering and recommendation systems, cross-language and multilingual information retrieval, distributed information retrieval and federated search, evaluation issues and test collections, formal models of information retrieval, information retrieval on mobile platforms, language models, indexing, structured documents and information retrieval, information classification and clustering, information extraction, information filtering and routing, meta-search, ranking aggregation and data fusion, natural language processing, performance issues of information retrieval systems including algorithms, data structures, optimization techniques, and scalability, question answering, single-document and multi-document summarization, text mining, topic detection, information retrieval in web search, user modeling, and usability, interactivity, and visualization issues in user studies.

《Foundations and Trends in Information Retrieval》发表关于信息检索领域的调查和教程文章，涵盖以下主题：信息检索架构、协作过滤和推荐系统的应用、跨语言和多语言信息检索、分布式信息检索和联合搜索、评估问题和测试集合、信息检索的形式模型、移动平台上的信息检索、语言模型、索引结构、结构化文档和信息检索、信息分类和聚类、信息提取、信息过滤和路由、元搜索、排名聚合和数据融合、自然语言处理、信息检索系统的性能问题，包括算法、数据结构、优化技术和可扩展性、问答系统、单文档和多文档摘要、文本挖掘、主题检测、网络搜索的信息检索、用户建模以及用户研究中的可用性、交互性和可视化问题。

Category	Quartile
计算机科学	2
计算机科学, 计算机信息系统	2

Efficient and Effective Tree-based and Neural Learning to Rank

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Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	1