高效的基于树和神经学习的排名

IF 8.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Foundations and Trends in Information Retrieval Pub Date : 2023-05-14 DOI:10.1561/1500000071
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
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引用次数: 2

摘要

作为信息检索研究人员,我们不仅开发解决难题的算法,而且还坚持对思想进行适当的、多方面的评估。例如,关于检索和排名这一基本主题的文献,在研究索引、检索算法和复杂机器学习排名器的有效性方面有着丰富的历史,同时量化它们的计算成本,从创建和训练到应用和推理。例如,十多年来对学习排序(LtR)中大型决策森林模型的有效训练和推理的研究证明了这一点。随着我们在更广泛的应用中向更复杂的深度学习模型迈进,效率问题再次以新的紧迫性重新浮出水面。事实上,效率不再局限于时间和空间;相反,它发现了新的、具有挑战性的维度,延伸到资源、样本和能源效率,对研究人员、用户和环境产生影响。本专著采取了一个步骤,以促进效率的研究在神经信息检索的时代,提供了一个全面的调查文献效率和有效性的排名,并在有限的程度上,检索。这本专著的灵感来自于基于神经网络的排名解决方案与其前身,基于决策森林的LtR模型的挑战之间存在的相似之处,以及迄今为止文献中所提供的解决方案之间的联系。我们相信,通过理解这些算法和数据结构解决方案的基础,以包含效率和有效性之间有争议的关系,人们可以更好地确定未来的方向,更有效地确定想法的优点。我们还提出了我们认为在检索和排名的效率和有效性方面的重要研究方向。
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Efficient and Effective Tree-based and Neural Learning to Rank

As information retrieval researchers, we not only develop algorithmic solutions to hard problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on the fundamental topic of retrieval and ranking, for instance, has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers, while at the same time quantifying their computational costs, from creation and training to application and inference. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in Learning to Rank (LtR). As we move towards even more complex, deep learning models in a wide range of applications, questions on efficiency have once again resurfaced with renewed urgency. Indeed, efficiency is no longer limited to time and space; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment.

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 LtR 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.

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来源期刊
Foundations and Trends in Information Retrieval
Foundations and Trends in Information Retrieval COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
39.10
自引率
0.00%
发文量
3
期刊介绍: The surge in research across all domains in the past decade has resulted in a plethora of new publications, causing an exponential growth in published research. Navigating through this extensive literature and staying current has become a time-consuming challenge. While electronic publishing provides instant access to more articles than ever, discerning the essential ones for a comprehensive understanding of any topic remains an issue. To tackle this, Foundations and Trends® in Information Retrieval - FnTIR - addresses the problem by publishing high-quality survey and tutorial monographs in the field. Each issue of Foundations and Trends® in Information Retrieval - FnT IR features a 50-100 page monograph authored by research leaders, covering tutorial subjects, research retrospectives, and survey papers that provide state-of-the-art reviews within the scope of the journal.
期刊最新文献
Multi-hop Question Answering User Simulation for Evaluating Information Access Systems Conversational Information Seeking Perspectives of Neurodiverse Participants in Interactive Information Retrieval Efficient and Effective Tree-based and Neural Learning to Rank
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