IDEAS home Printed from
   My bibliography  Save this article

Exploring an opinion network for taste prediction: An empirical study


  • Blattner, Marcel
  • Zhang, Yi-Cheng
  • Maslov, Sergei


We develop a simple statistical method to find affinity relations in a large opinion network which is represented by a very sparse matrix. These relations allow us to predict missing matrix elements. We test our method on the Eachmovie data of thousands of movies and viewers. We found that significant prediction precision can be achieved and it is rather stable. There is an intrinsic limit to further improve the prediction precision by collecting more data, implying perfect prediction can never obtain via statistical means.

Suggested Citation

  • Blattner, Marcel & Zhang, Yi-Cheng & Maslov, Sergei, 2007. "Exploring an opinion network for taste prediction: An empirical study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 753-758.
  • Handle: RePEc:eee:phsmap:v:373:y:2007:i:c:p:753-758
    DOI: 10.1016/j.physa.2006.04.121

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Paul Resnick & Neophytos Iacovou & Mitesh Suchak & Peter Bergstrom & John Riedl, 1994. "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Working Paper Series 165, MIT Center for Coordination Science.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Borrero, Juan D. & Gualda Caballero, Estrella, 2013. "Crawling Big Data in a New Frontier for Socioeconomic Research: Testing with Social Tagging," Journal of Spatial and Organizational Dynamics, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve, vol. 1(1), pages 3-24.
    2. Liu, Ji & Deng, Guishi, 2009. "Link prediction in a user–object network based on time-weighted resource allocation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3643-3650.
    3. Zhong, Li-Xin & Xu, Wen-Juan & Chen, Rong-Da & Zhong, Chen-Yang & Qiu, Tian & Shi, Yong-Dong & Wang, Li-Liang, 2016. "A generalized voter model with time-decaying memory on a multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 95-105.
    4. Cesário, Marisa & Noronha Vaz, Maria Teresa, 2013. "Localised Assets and Small-Firms’ Technological Capabilities," Spatial and Organizational Dynamics Discussion Papers 2013-7, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve.


    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:eee:phsmap:v:373:y:2007:i:c:p:753-758. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.