Semantic Models for the First-Stage Retrieval: A Comprehensive Review

doi:10.1145/3486250

	Semantic Models for the First-Stage Retrieval: A Comprehensive Review
	Guo, Jiafeng 1,2; Cai, Yinqiong 1,2; Fan, Yixing 1,2; Sun, Fei 3; Zhang, Ruqing 1,2; Cheng, Xueqi 1,2
	2022-10-01
发表期刊	ACM TRANSACTIONS ON INFORMATION SYSTEMS
ISSN	1046-8188
卷号	40 期号:4 页码:42
摘要	Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents and latter stages attempt to re-rank those candidates. Unlike re-ranking stages going through quick technique shifts over the past decades, the first-stage retrieval has long been dominated by classical term-based models. Unfortunately, these models suffer from the vocabulary mismatch problem, which may block re-ranking stages from relevant documents at the very beginning. Therefore, it has been a long-term desire to build semantic models for the first-stage retrieval that can achieve high recall efficiently. Recently, we have witnessed an explosive growth of research interests on the first-stage semantic retrieval models. We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development. In this article, we describe the current landscape of the first-stage retrieval models under a unified framework to clarify the connection between classical term-based retrieval methods, early semantic retrieval methods, and neural semantic retrieval methods. Moreover, we identify some open challenges and envision some future directions, with the hope of inspiring more research on these important yet less investigated topics.
关键词	Semantic retrieval models information retrieval survey
DOI	10.1145/3486250
收录类别	SCI
语种	英语
资助项目	Beijing Academy of Artificial Intelligence (BAAI)[BAAI2019ZD0306] ; National Natural Science Foundation of China (NSFC)[61902381] ; National Natural Science Foundation of China (NSFC)[62006218] ; National Natural Science Foundation of China (NSFC)[61872338] ; Youth Innovation Promotion Association CAS[20144310] ; Youth Innovation Promotion Association CAS[2021100] ; Lenovo-CAS Joint Lab Youth Scientist Project ; Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission[cstc2017jcyjBX0059]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000796738200003
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：33[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19567
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Fan, Yixing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China 3.Alibaba Grp, Beijing 100102, Peoples R China
推荐引用方式 GB/T 7714	Guo, Jiafeng,Cai, Yinqiong,Fan, Yixing,et al. Semantic Models for the First-Stage Retrieval: A Comprehensive Review[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2022,40(4):42.
APA	Guo, Jiafeng,Cai, Yinqiong,Fan, Yixing,Sun, Fei,Zhang, Ruqing,&Cheng, Xueqi.(2022).Semantic Models for the First-Stage Retrieval: A Comprehensive Review.ACM TRANSACTIONS ON INFORMATION SYSTEMS,40(4),42.
MLA	Guo, Jiafeng,et al."Semantic Models for the First-Stage Retrieval: A Comprehensive Review".ACM TRANSACTIONS ON INFORMATION SYSTEMS 40.4(2022):42.