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A Deep Look into neural ranking models for information retrieval

文献类型：期刊论文

作者Guo, Jiafeng1,2; Fan, Yixing1,2; Pang, Liang1,2; Yang, Liu3; Ai, Qingyao3; Zamani, Hamed3; Wu, Chen1,2; Croft, W. Bruce3; Cheng, Xueqi1,2
刊名INFORMATION PROCESSING & MANAGEMENT
出版日期2020-11-01
卷号57期号:6页码:20
关键词Neural ranking model Information retrieval Survey
ISSN号0306-4573
DOI10.1016/j.ipm.2019.102067
英文摘要Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.
资助项目National Natural Science Foundation of China (NSFC)[61425016] ; National Natural Science Foundation of China (NSFC)[61722211] ; Youth Innovation Promotion Association CAS[20144310] ; UMass Amherst Center for Intelligent Information Retrieval ; NSF[IIS-1715095]
WOS研究方向Computer Science ; Information Science & Library Science
语种英语
WOS记录号WOS:000582206800010
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/16021]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Guo, Jiafeng
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
3.Univ Massachusetts, Ctr Intelligent Informat Retrieval, Amherst, MA 01003 USA
推荐引用方式
GB/T 7714		 Guo, Jiafeng,Fan, Yixing,Pang, Liang,et al. A Deep Look into neural ranking models for information retrieval[J]. INFORMATION PROCESSING & MANAGEMENT,2020,57(6):20.
APA Guo, Jiafeng.,Fan, Yixing.,Pang, Liang.,Yang, Liu.,Ai, Qingyao.,...&Cheng, Xueqi.(2020).A Deep Look into neural ranking models for information retrieval.INFORMATION PROCESSING & MANAGEMENT,57(6),20.
MLA Guo, Jiafeng,et al."A Deep Look into neural ranking models for information retrieval".INFORMATION PROCESSING & MANAGEMENT 57.6(2020):20.

入库方式： OAI收割

来源：计算技术研究所

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