Understanding Persuasion Cascades in Online Product Rating Systems
Refereed conference paper presented and published in conference proceedings


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摘要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 "message-based persuasion" lead to "biased" product ratings in a cascading manner (we call this the persuasion cascade). This paper investigates: (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? 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 the data from Amazon and TripAdvisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.
著者Xie Hong, Yongkun Li, John C.S. Lui
會議名稱Thirty-Third AAAI Conference on Artificial Intelligence (AAAI)
會議開始日27.01.2019
會議完結日01.02.2019
會議地點Honolulu, HI
會議國家/地區美國
會議論文集題名Thirty-Third AAAI Conference on Artificial Intelligence (AAAI)
出版年份2019
月份1
頁次5490 - 5497
國際標準書號978-1-57735-809-1
語言美式英語

上次更新時間 2021-23-11 於 01:09