| Understanding and Predicting Users' Rating Behavior: A Cognitive Perspective | |
Li, Qiudan1,2 ; Zeng, Daniel Dajun1,2,3; Xu, David Jingjun4; Liu, Ruoran1,2,3; Yao, Riheng1,2,3
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| Source Publication | INFORMS JOURNAL ON COMPUTING
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| ISSN | 1091-9856 |
| 2020-09-01 | |
| Volume | 32Issue:4Pages:996-1011 |
| Corresponding Author | Li, Qiudan(qiudan.li@ia.ac.cn) |
| Abstract | 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. |
| Keyword | rating behavior analysis cognitive theory review content aspect rating rating prediction |
| DOI | 10.1287/ijoc.2019.0919 |
| WOS Keyword | INFORMATION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 |
| WOS Research Area | Computer Science ; Operations Research & Management Science |
| WOS Subject | Computer Science, Interdisciplinary Applications ; Operations Research & Management Science |
| WOS ID | WOS:000591904200010 |
| Publisher | INFORMS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/42717 |
| Collection | 复杂系统管理与控制国家重点实验室_互联网大数据与信息安全 |
| Corresponding Author | Li, Qiudan |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen 518110, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.City Univ Hong Kong, Coll Business, Dept Informat Syst, Hong Kong, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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. |
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