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Exploiting shopping and reviewing behavior to re-score online evaluations

Authors:

Rong Zhang,

ChaoFeng Sha,

Minqi Zhou,

Aoying ZhouAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 649 - 650

https://doi.org/10.1145/2187980.2188171

Published: 16 April 2012 Publication History

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Abstract

Analysis to product reviews has attracted great attention from both academia and industry. Generally the evaluation scores of reviews are used to generate the average scores of products and shops for future potential users. However, in the real world, there is the inconsistency problem between the evaluation scores and review content, and some customers do not give out fair reviews. In this work, we focus on detecting the credibility of customers by analyzing online shopping and review behaviors, and then we re-score the reviews for products and shops. In the end, we evaluate our algorithm based on the real data set from Taobao, the biggest E-commerce site in China.

References

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G. Wang, S. Xie, B. Liu, and P. S. Yu. Review graph based online store review spammer detection. In Proc. of ICDM, 2011.

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Cited By

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Abdulqader MNamoun AAlsaawy Y(2022)Fake Online Reviews: A Unified Detection Model Using Deception TheoriesIEEE Access10.1109/ACCESS.2022.322763110(128622-128655)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3227631
(2018)Detecting spammers using review graphInternational Journal of High Performance Computing and Networking10.5555/3141079.314108210:4-5(269-278)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.5555/3141079.3141082
Zhang XXie GLi DKang R(2018)Reliability Evaluation Based on Sentiment Analysis of Online Comment2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS)10.1109/ICRMS.2018.00026(88-91)Online publication date: Oct-2018
https://doi.org/10.1109/ICRMS.2018.00026
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Index Terms

Exploiting shopping and reviewing behavior to re-score online evaluations
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Abdulqader MNamoun AAlsaawy Y(2022)Fake Online Reviews: A Unified Detection Model Using Deception TheoriesIEEE Access10.1109/ACCESS.2022.322763110(128622-128655)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3227631
(2018)Detecting spammers using review graphInternational Journal of High Performance Computing and Networking10.5555/3141079.314108210:4-5(269-278)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.5555/3141079.3141082
Zhang XXie GLi DKang R(2018)Reliability Evaluation Based on Sentiment Analysis of Online Comment2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS)10.1109/ICRMS.2018.00026(88-91)Online publication date: Oct-2018
https://doi.org/10.1109/ICRMS.2018.00026
Wu XDong YTao JHuang CChawla N(2017)Reliable fake review detection via modeling temporal and behavioral patterns2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8257963(494-499)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8257963
Chen GCai WHuang JJiao X(2016)Uncovering and Characterizing Internet Water Army in Online Forums2016 IEEE First International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2016.77(169-178)Online publication date: Jun-2016
https://doi.org/10.1109/DSC.2016.77
Zhang RZhang ZHe XZhou A(2015)Dish comment summarization based on bilateral topic analysis2015 IEEE 31st International Conference on Data Engineering10.1109/ICDE.2015.7113308(483-494)Online publication date: Apr-2015
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Zhang RYu WSha CHe XZhou A(2015)Product-oriented review summarization and scoringFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-014-3492-09:2(210-223)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1007/s11704-014-3492-0
Liang DLiu XShen H(2014)Detecting spam reviewers by combing reviewer feature and relationshipProceedings 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)10.1109/ICCSS.2014.6961824(102-107)Online publication date: Oct-2014
https://doi.org/10.1109/ICCSS.2014.6961824
Dong BLiu QFu YZhang L(2014)A Research of Taobao Cheater DetectionDigital Services and Information Intelligence10.1007/978-3-662-45526-5_31(338-345)Online publication date: 2014
https://doi.org/10.1007/978-3-662-45526-5_31
Peng QChen J(2013)Detecting Store Review Spammer via Review RelationshipAdvanced Materials Research10.4028/www.scientific.net/AMR.718-720.2153718-720(2153-2158)Online publication date: Jul-2013
https://doi.org/10.4028/www.scientific.net/AMR.718-720.2153

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Shopping Behavior and Consumer Preference for Store Price Format: Why "Large Basket" Shoppers Prefer EDLP

Shopping Behavior and Consumer Preference for Store Price Format: Why "Large Basket" Shoppers Prefer EDLP

Online Shopping Behavior: A Study of Factors Influencing Consumer Satisfaction on Online viz-a-viz Conventional Store Shopping

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