Should We Reach for the Stars? Examining the Convergence Between Online Product Ratings and Objective Product Quality and Their Impacts on Sales Performance

2018 ◽  
Vol 3 (2) ◽  
pp. 167-183
Author(s):  
Sarah >Köcher ◽  
Sören >Köcher
Keyword(s):  
Product Quality ◽  
Sales Performance ◽  
Online Product Ratings ◽  
Product Ratings

scholarly journals Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations

2018 ◽  
Author(s):  
Xiaoying Zhang ◽  
Hong Xie ◽  
Junzhou Zhao ◽  
John C.S. Lui
Keyword(s):  
Product Quality ◽  
Factor Model ◽  
Experimental Studies ◽  
Purchase Decisions ◽  
Contrast Effects ◽  
Rating Systems ◽  
Micro Level ◽  
Online Product Ratings ◽  
Product Ratings ◽  
Product Rating

The unbiasedness of online product ratings, an important property to ensure that users’ ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” the distortions from historical ratings in each single rating (or at the micro-level), and perform the “debiasing operations” in real rating systems are the main objectives of this work. Using 42 million real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the “Assimilate-Contrast” theory. However, none of the existing works on modeling historical ratings’ influence have taken this into account, and this motivates us to propose the Histori- cal Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users’ real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.

Dynamic trends in online product ratings: A diagnostic utility explanation

2018 ◽  
Vol 87 ◽  
pp. 80-89 ◽  
Author(s):  
Fang Wang ◽  
Kalyani Menon ◽  
Chatura Ranaweera
Keyword(s):  
Diagnostic Utility ◽  
Online Product Ratings ◽  
Product Ratings

Social Influence Effects in Online Product Ratings

2012 ◽  
Vol 76 (5) ◽  
pp. 70-88 ◽  
Author(s):  
Shrihari Sridhar ◽  
Raji Srinivasan
Keyword(s):  
Social Influence ◽  
Online Product Ratings ◽  
Product Ratings

scholarly journals Understanding Persuasion Cascades in Online Product Rating Systems

2019 ◽  
Vol 33 ◽  
pp. 5490-5497
Author(s):  
Hong Xie ◽  
Yongkun Li ◽  
John C.S. Lui
Keyword(s):  
Product Quality ◽  
Estimation Error ◽  
Opinion Dynamics ◽  
Estimation Accuracy ◽  
Matrix Perturbation ◽  
Rating Systems ◽  
Quality Estimation ◽  
Scoring Rule ◽  
Product Ratings ◽  
Product Rating

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 “messagebased 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.

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