research-article

Understanding Assimilation-contrast Effects in Online Rating Systems: Modelling, Debiasing, and Applications

Authors:

Xiaoying Zhang,

John C. S. LuiAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 38, Issue 1

Article No.: 2, Pages 1 - 25

https://doi.org/10.1145/3362651

Published: 17 October 2019 Publication History

Abstract

“Unbiasedness,” which is an important property to ensure that users’ ratings indeed reflect their true evaluations of products, is vital both in shaping consumer purchase decisions and providing reliable recommendations in online rating systems. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” historical distortions in each single rating (or at the micro-level), and perform the “debiasing operations” are our main objective. Using 42M real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios, which can be further explained by a well-known psychological argument: the “Assimilate-Contrast” theory. This motivates us to propose the Historical Influence Aware Latent Factor Model (HIALF), the “first” model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF allows us to study the influence patterns of historical ratings from a modelling perspective, which perfectly matches the assimilation and contrast effects observed in experiments. Moreover, HIALF achieves significant improvements in predicting subsequent ratings and characterizing relationships in ratings. It also contributes to better recommendations, wiser consumer purchase decisions, and deeper understanding of historical distortions in both honest rating and misbehaving rating settings.

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Index Terms

Understanding Assimilation-contrast Effects in Online Rating Systems: Modelling, Debiasing, and Applications
1. Information systems
  1. Information systems applications
    1. Data mining
2. Social and professional topics
  1. User characteristics

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 38, Issue 1

January 2020

301 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3368262

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2019 ACM.

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Publication History

Published: 17 October 2019

Accepted: 01 August 2019

Revised: 01 July 2019

Received: 01 December 2018

Published in TOIS Volume 38, Issue 1

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Jia PWang PLi RZhao JFeng JWang XGuan X(2024)A Compact and Accurate Sketch for Estimating a Large Range of Set Difference Cardinalities2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00110(1338-1351)Online publication date: 13-May-2024
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