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.

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.

Understanding Persuasion Cascades in Online Product Rating Systems: Modeling, Analysis, and Inference

2021 ◽  
Vol 15 (3) ◽  
pp. 1-29
Author(s):  
Hong Xie ◽  
Mingze Zhong ◽  
Yongkun Li ◽  
John C. S. Lui
Keyword(s):  
Product Quality ◽  
Real World ◽  
Estimation Error ◽  
Estimation Accuracy ◽  
Rating Systems ◽  
Real World Data ◽  
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 “ message-based persuasion ” lead to “ biased ” product ratings in a cascading manner (we call this the persuasion cascade ). This article 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 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.

Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application

2021 ◽  
Author(s):  
Peiyu Chen ◽  
Lorin M. Hitt ◽  
Yili Hong ◽  
Shinyi Wu
Keyword(s):  
Product Category ◽  
Product Type ◽  
Horizontal Differentiation ◽  
Review System ◽  
Product Positioning ◽  
Rating Data ◽  
Business Operations ◽  
Online Product Ratings ◽  
Product Ratings ◽  
Product Rating

Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In our paper, we propose a set of theory-grounded data-driven measures that allow us to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. We used product rating data from Amazon.com to illustrate the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, whereas ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.

Development of a Measure of Calculative Mindset (CM): Establishing a Nomological Net and Predictive Utility of the CM Measure

2021 ◽  
pp. 003329412110102
Author(s):  
Joongseo Kim ◽  
I. M. Jawahar ◽  
Brigitte Steinheider ◽  
Thomas Stone ◽  
Brandon Ferrell
Keyword(s):  
Organizational Citizenship ◽  
Factor Model ◽  
Five Factor Model ◽  
Experimental Studies ◽  
Field Research ◽  
Performance Outcomes ◽  
Work Behavior ◽  
Hierarchical Regression ◽  
Organizational Contexts ◽  
Personality Research

A calculative mindset (CM) describes the tendency to analyze and convert qualitative social values into numeric or monetary metrics and is a predisposition that shapes behaviors and actions of the employee. CM has been manipulated in experimental studies, but it has not been investigated in field research due to the absence of a scale to measure CM. In study 1, we followed Hinkin’s scale development protocol to conceptualize, develop, and validate a measure of CM to facilirate research in organizational contexts. In Study 2, we examined the relationship between CM and measures of performance, counterproductive work behavior (CWB), organizational citizenship behaviors (OCB), and in role-performance (IRP). Results from hierarchical regression analyses indicate that CM is related to these performance outcomes and explains incremental variance over established measures of the Five-Factor Model of personality. Implications for personality research, selection of human resources, and facilitation of an ethical workplace are discussed.

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