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



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)
會議地點Honolulu, HI
會議論文集題名Thirty-Third AAAI Conference on Artificial Intelligence (AAAI)
頁次5490 - 5497

上次更新時間 2021-02-12 於 00:37