IDEAS home Printed from https://ideas.repec.org/a/bla/jinfst/v71y2020i7p784-799.html
   My bibliography  Save this article

Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness

Author

Listed:
  • Wenyi Tay
  • Xiuzhen Zhang
  • Sarvnaz Karimi

Abstract

The star‐rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the “true quality” of products and services is challenging. The mean‐rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the “helpfulness” social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on “helpfulness” achieve better performance than the statistical and heuristic baseline approaches.

Suggested Citation

  • Wenyi Tay & Xiuzhen Zhang & Sarvnaz Karimi, 2020. "Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(7), pages 784-799, July.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:7:p:784-799
    DOI: 10.1002/asi.24297
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.24297
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.24297?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alton Y.K. Chua & Snehasish Banerjee, 2015. "Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 354-362, February.
    2. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    2. Guha Majumder, Madhumita & Dutta Gupta, Sangita & Paul, Justin, 2022. "Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis," Journal of Business Research, Elsevier, vol. 150(C), pages 147-164.
    3. Xiaomo Liu & G. Alan Wang & Weiguo Fan & Zhongju Zhang, 2020. "Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis," Information Systems Research, INFORMS, vol. 31(3), pages 731-752, September.
    4. Meek, Stephanie & Wilk, Violetta & Lambert, Claire, 2021. "A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews," Journal of Business Research, Elsevier, vol. 125(C), pages 354-367.
    5. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
    6. Hossin Md Altab & Mu Yinping & Hosain Md Sajjad & Adasa Nkrumah Kofi Frimpong & Michelle Frempomaa Frempong & Stephen Sarfo Adu-Yeboah, 2022. "Understanding Online Consumer Textual Reviews and Rating: Review Length With Moderated Multiple Regression Analysis Approach," SAGE Open, , vol. 12(2), pages 21582440221, June.
    7. Wu, Xiaoyue & Jin, Liyin & Xu, Qian, 2021. "Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews," Journal of Retailing, Elsevier, vol. 97(2), pages 238-250.
    8. Xunhua Guo & Guoqing Chen & Cong Wang & Qiang Wei & Zunqiang Zhang, 2021. "Calibration of Voting-Based Helpfulness Measurement for Online Reviews: An Iterative Bayesian Probability Approach," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 246-261, January.
    9. Srikanth Parameswaran & Pubali Mukherjee & Rohit Valecha, 2023. "I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews," Information Systems Frontiers, Springer, vol. 25(2), pages 853-870, April.
    10. Duan, Yongrui & Liu, Tonghui & Mao, Zhixin, 2022. "How online reviews and coupons affect sales and pricing: An empirical study based on e-commerce platform," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    11. Bi, Sheng & Liu, Zhiying & Usman, Khalid, 2017. "The influence of online information on investing decisions of reward-based crowdfunding," Journal of Business Research, Elsevier, vol. 71(C), pages 10-18.
    12. Hsiang-Ming Lee & Tsai Chen & Ya-Hui Hsu & Yu-Chi Wu, 2018. "Effect Of Complementary Product Fit And Brand Awareness On Brand Attitude After M&As: Word Of Mouth As A Moderator," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 12(1), pages 51-67.
    13. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    14. Wang, Feng & Liu, Xuefeng & Fang, Eric (Er), 2015. "User Reviews Variance, Critic Reviews Variance, and Product Sales: An Exploration of Customer Breadth and Depth Effects," Journal of Retailing, Elsevier, vol. 91(3), pages 372-389.
    15. Kavita Rawat & Sunita Kumar, 2022. "A Meta-Analysis on the Determinants of Online Product Reviews with Moderating Effect of Product Type," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 11, November.
    16. Hu, Xin & He, Liuyi & Liu, Junjun, 2022. "Status reinforcing: Unintended rating bias on online shopping platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    17. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    18. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    19. Lee, Na Young & Noble, Stephanie M. & Zablah, Alex R., 2020. "So distant, yet useful: The impact of distal stories on customers’ service expectations," Journal of Business Research, Elsevier, vol. 113(C), pages 230-242.
    20. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics," Post-Print halshs-01923196, HAL.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jinfst:v:71:y:2020:i:7:p:784-799. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.