Knowledge Commons of Institute of Automation，CAS
| 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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| 发表期刊 | INFORMS JOURNAL ON COMPUTING
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| ISSN | 1091-9856 |
| 2020-09-01 | |
| 卷号 | 32期号:4页码:996-1011 |
| 通讯作者 | Li, Qiudan(qiudan.li@ia.ac.cn) |
| 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. |
| 关键词 | rating behavior analysis cognitive theory review content aspect rating rating prediction |
| DOI | 10.1287/ijoc.2019.0919 |
| 关键词[WOS] | INFORMATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| 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] |
| 项目资助者 | 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研究方向 | Computer Science ; Operations Research & Management Science |
| WOS类目 | Computer Science, Interdisciplinary Applications ; Operations Research & Management Science |
| WOS记录号 | WOS:000591904200010 |
| 出版者 | INFORMS |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/42717 |
| 专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
| 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.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 |
| 第一作者单位 | 中国科学院自动化研究所 |
| 通讯作者单位 | 中国科学院自动化研究所 |
| 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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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