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

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AbstractOnline 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.
All Author(s) ListXie Hong, Yongkun Li, John C.S. Lui
Name of ConferenceThirty-Third AAAI Conference on Artificial Intelligence (AAAI)
Start Date of Conference27/01/2019
End Date of Conference01/02/2019
Place of ConferenceHonolulu, HI
Country/Region of ConferenceUnited States of America
Proceedings TitleThirty-Third AAAI Conference on Artificial Intelligence (AAAI)
Pages5490 - 5497
LanguagesEnglish-United States

Last updated on 2021-02-12 at 00:37