IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v21y2010i10ns0129183110015786.html
   My bibliography  Save this article

Can Dissimilar Users Contribute To Accuracy And Diversity Of Personalized Recommendation?

Author

Listed:
  • WEI ZENG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • MING-SHENG SHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • QIAN-MING ZHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • LINYUAN LÜ

    (Department of Physics, University of Fribourg, Chemin du Musée 3, Fribourg CH-1700, Switzerland)

  • TAO ZHOU

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
    Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, P. R. China)

Abstract

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

Suggested Citation

  • Wei Zeng & Ming-Sheng Shang & Qian-Ming Zhang & Linyuan Lü & Tao Zhou, 2010. "Can Dissimilar Users Contribute To Accuracy And Diversity Of Personalized Recommendation?," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(10), pages 1217-1227.
    • Handle: RePEc:wsi:ijmpcx:v:21:y:2010:i:10:n:s0129183110015786
    DOI: 10.1142/S0129183110015786
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0129183110015786
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0129183110015786?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
    ---><---

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

    Citations

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


    Cited by:

    1. Wei Zeng & An Zeng & Hao Liu & Ming-Sheng Shang & Yi-Cheng Zhang, 2014. "Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-8, October.
    2. Wei Zeng & An Zeng & Ming-Sheng Shang & Yi-Cheng Zhang, 2013. "Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    3. YingSi Zhao & Bo Shen, 2016. "Empirical Study of User Preferences Based on Rating Data of Movies," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-19, January.

    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:wsi:ijmpcx:v:21:y:2010:i:10:n:s0129183110015786. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijmpc/ijmpc.shtml .

    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.