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Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>\n            Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google Play Store, and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called \u201c\n            <jats:italic>message-based persuasion<\/jats:italic>\n            \u201d lead to \u201c\n            <jats:italic>biased<\/jats:italic>\n            \u201d product ratings in a cascading manner (we call this the\n            <jats:italic>persuasion cascade<\/jats:italic>\n            ). This article investigates:\n            <jats:italic>(1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights?<\/jats:italic>\n            We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on both synthetic data and real-world data from Amazon and TripAdvisor. Experiment results show that our inference algorithm has a high accuracy. Furthermore, persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.\n          <\/jats:p>","DOI":"10.1145\/3440887","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T15:42:54Z","timestamp":1619019774000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Understanding Persuasion Cascades in Online Product Rating Systems: Modeling, Analysis, and Inference"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-7210","authenticated-orcid":false,"given":"Hong","family":"Xie","sequence":"first","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingze","family":"Zhong","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongkun","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John C. S.","family":"Lui","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 2016 INTRS WORKSHOP.","author":"Adomavicius Gediminas","year":"2016","unstructured":"Gediminas Adomavicius , Jesse Bockstedt , Shawn P. Curley , and Jingjing Zhang . 2016 . Understanding effects of personalized vs. aggregate ratings on user preferences . In Proceedings of the 2016 INTRS WORKSHOP. Gediminas Adomavicius, Jesse Bockstedt, Shawn P. Curley, and Jingjing Zhang. 2016. Understanding effects of personalized vs. aggregate ratings on user preferences. In Proceedings of the 2016 INTRS WORKSHOP."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/0047287516643185"},{"key":"e_1_2_1_3_1","unstructured":"Jonah Berger. 2012. 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