Understanding and Predicting Users' Rating Behavior: A Cognitive Perspective

doi:10.1287/ijoc.2019.0919

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Understanding and Predicting Users' Rating Behavior: A Cognitive Perspective
	Li, Qiudan 1,4; Zeng, Daniel Dajun 1,3,4; Xu, David Jingjun 2; Liu, Ruoran 1,3,4; Yao, Riheng 1,3,4
刊名	INFORMS JOURNAL ON COMPUTING
	2020-09-01
卷号	32 期号:4 页码:996-1011
关键词	rating behavior analysis cognitive theory review content aspect rating rating prediction
ISSN号	1091-9856
DOI	10.1287/ijoc.2019.0919
通讯作者	Li, Qiudan(qiudan.li@ia.ac.cn)
英文摘要	Online reviews are playing an increasingly important role in understanding and predicting users' rating behavior, which brings great opportunities for users and organizations to make better decisions. In recent years, rating prediction has become a research hotspot. Existing research primarily focuses on generating content representation based on context information and using the overall rating score to optimize the semantics of the content, which largely ignores aspect ratings reflecting users' feelings about more specific attributes of a product and semantic associations among aspect ratings, words, and sentences. Cognitive theory research has shown that users evaluate and rate products following the part-whole pattern; namely, they use aspect ratings to explicitly express sentiments toward aspect attributes of products and then describe those attributes in detail through the corresponding opinion words and sentences. In this paper, we develop a deep learning-based method for understanding and predicting users' rating behavior, which adopts the hierarchical attention mechanism to unify the explicit aspect ratings and review contents. We conducted experiments using data collected from two real-world review sites and found that our proposed approach significantly outperforms existing methods. Experiments also show that the performance advantage of the proposed approach mainly comes from the high-quality representation of review content and the effective integration of aspect ratings. A user study empirically shows that aspect ratings influence users' perceived review helpfulness and reduce users' cognitive effort in understanding the overall score given for a product. The research contributes to the rating behavior analysis literature and has significant practical implications.
资助项目	National Key R&D Program of China[2016QY02D0305] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[61671450] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-XH-2017-3] ; Digital Innovation Laboratory of the Department of Information Systems ; City University of Hong Kong[7200565]
WOS关键词	INFORMATION
WOS研究方向	Computer Science ; Operations Research & Management Science
语种	英语
出版者	INFORMS
WOS记录号	WOS:000591904200010
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Key Research Program of the Chinese Academy of Sciences ; Digital Innovation Laboratory of the Department of Information Systems ; City University of Hong Kong
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42716]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Li, Qiudan
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.City Univ Hong Kong, Coll Business, Dept Informat Syst, Hong Kong, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen 518110, Peoples R China
推荐引用方式 GB/T 7714	Li, Qiudan,Zeng, Daniel Dajun,Xu, David Jingjun,et al. Understanding and Predicting Users' Rating Behavior: A Cognitive Perspective[J]. INFORMS JOURNAL ON COMPUTING,2020,32(4):996-1011.
APA	Li, Qiudan,Zeng, Daniel Dajun,Xu, David Jingjun,Liu, Ruoran,&Yao, Riheng.(2020).Understanding and Predicting Users' Rating Behavior: A Cognitive Perspective.INFORMS JOURNAL ON COMPUTING,32(4),996-1011.
MLA	Li, Qiudan,et al."Understanding and Predicting Users' Rating Behavior: A Cognitive Perspective".INFORMS JOURNAL ON COMPUTING 32.4(2020):996-1011.