IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v25y2014i3p569-589.html
   My bibliography  Save this article

The “Most Popular News” Recommender: Count Amplification and Manipulation Resistance

Author

Listed:
  • Shankar Prawesh

    (College of Business, University of South Florida, Tampa, Florida 33620)

  • Balaji Padmanabhan

    (College of Business, University of South Florida, Tampa, Florida 33620)

Abstract

A broad motivation for our research is to build manipulation resistant news recommender systems. There are several algorithms that can be used to generate news recommendations, and the strategies for manipulation resistance are likely specific to the algorithm (or class of algorithm) used. In this paper, we will focus on a common method used on the front page by many media sites of recommending the N most popular articles (e.g., New York Times, BBC, CNN, Wall Street Journal all prominently use this). We show that whereas recommendation of the N most read articles is easily susceptible to manipulation, a probabilistic variant is more robust to common manipulation strategies. Furthermore, for the “ N most popular” recommender, probabilistic selection has other desirable properties. Specifically, the ( N + 1) th article, which may have just missed making the cut-off, is unduly penalized under common user models. Small differences are easily amplified initially, an observation that can be used by manipulators. Probabilistic selection, on the other hand, creates no such artificial penalty. We use classical results from urn models to derive theoretical results for special cases and study specific properties of the probabilistic recommender.

Suggested Citation

  • Shankar Prawesh & Balaji Padmanabhan, 2014. "The “Most Popular News” Recommender: Count Amplification and Manipulation Resistance," Information Systems Research, INFORMS, vol. 25(3), pages 569-589, September.
    • Handle: RePEc:inm:orisre:v:25:y:2014:i:3:p:569-589
    DOI: 10.1287/isre.2014.0529
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2014.0529
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2014.0529?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. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    3. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    4. Benjamin Van Roy & Xiang Yan, 2010. "Manipulation Robustness of Collaborative Filtering," Management Science, INFORMS, vol. 56(11), pages 1911-1929, November.
    5. Rogers, Everett M, 1976. "New Product Adoption and Diffusion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(4), pages 290-301, March.
    6. Jong-Seok Lee & Dan Zhu, 2012. "Shilling Attack Detection---A New Approach for a Trustworthy Recommender System," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 117-131, February.
    7. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, October.
    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. Tianshu Sun & Sean J. Taylor, 2020. "Displaying things in common to encourage friendship formation: A large randomized field experiment," Quantitative Marketing and Economics (QME), Springer, vol. 18(3), pages 237-271, September.
    2. Anthony Heyes & Sandeep Kapur, 2012. "Customer Anger and Incentives for Quality Provision," Birkbeck Working Papers in Economics and Finance 1215, Birkbeck, Department of Economics, Mathematics & Statistics.
    3. Blazquez-Soriano, Amparo & Ramos-Sandoval, Rosmery, 2022. "Information transfer as a tool to improve the resilience of farmers against the effects of climate change: The case of the Peruvian National Agrarian Innovation System," Agricultural Systems, Elsevier, vol. 200(C).
    4. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 67-77, April.
    5. Martin L. Weitzman, 2015. "A Voting Architecture for the Governance of Free-Driver Externalities, with Application to Geoengineering," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(4), pages 1049-1068, October.
    6. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    7. Hwang, ShinYoung & Kim Seongcheol, 2017. "What triggers the use of mIM service provider’s sequel O2O service extensions?," 14th ITS Asia-Pacific Regional Conference, Kyoto 2017: Mapping ICT into Transformation for the Next Information Society 168494, International Telecommunications Society (ITS).
    8. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    9. Guo Weilong & Minca Andreea & Wang Li, 2016. "The topology of overlapping portfolio networks," Statistics & Risk Modeling, De Gruyter, vol. 33(3-4), pages 139-155, December.
    10. Roman Inderst & Marco Ottaviani, 2013. "Sales Talk, Cancellation Terms and the Role of Consumer Protection," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(3), pages 1002-1026.
    11. Zhan (Michael) Shi & T. S. Raghu, 2020. "An Economic Analysis of Product Recommendation in the Presence of Quality and Taste-Match Heterogeneity," Information Systems Research, INFORMS, vol. 31(2), pages 399-411, June.
    12. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    13. Weeds, Helen, 2012. "Superstars and the long tail: The impact of technology on market structure in media industries," Information Economics and Policy, Elsevier, vol. 24(1), pages 60-68.
    14. Bernd (B.) Heidergott & Jia-Ping Huang & Ines (I.) Lindner, 2018. "Naive Learning in Social Networks with Random Communication," Tinbergen Institute Discussion Papers 18-018/II, Tinbergen Institute.
    15. Johannes M. Bauer & Michael Latzer, 2016. "The economics of the Internet: an overview," Chapters, in: Johannes M. Bauer & Michael Latzer (ed.), Handbook on the Economics of the Internet, chapter 1, pages 3-20, Edward Elgar Publishing.
    16. Heyes, Anthony & Kapur, Sandeep, 2012. "Angry customers, e-word-of-mouth and incentives for quality provision," Journal of Economic Behavior & Organization, Elsevier, vol. 84(3), pages 813-828.
    17. Kobayashi, Teruyoshi & Takaguchi, Taro, 2018. "Identifying relationship lending in the interbank market: A network approach," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 20-36.
    18. Konstantinos Antoniadis & Kostas Zafiropoulos & Vasiliki Vrana, 2016. "A Method for Assessing the Performance of e-Government Twitter Accounts," Future Internet, MDPI, vol. 8(2), pages 1-18, April.
    19. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    20. Maness, Michael & Cirillo, Cinzia, 2016. "An indirect latent informational conformity social influence choice model: Formulation and case study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 75-101.

    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:inm:orisre:v:25:y:2014:i:3:p:569-589. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.