IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v24y2013i04ns0129183113500228.html
   My bibliography  Save this article

Social Interest For User Selecting Items In Recommender Systems

Author

Listed:
  • DA-CHENG NIE

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

  • MING-JING DING

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

  • YAN FU

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

  • JUN-LIN ZHOU

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

  • ZI-KE ZHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
    Institute of Information Economy, Hangzhou Normal University, Hangzhou 310036, P. R. China)

Abstract

Recommender systems have developed rapidly and successfully. The system aims to help users find relevant items from a potentially overwhelming set of choices. However, most of the existing recommender algorithms focused on the traditional user-item similarity computation, other than incorporating the social interests into the recommender systems. As we know, each user has their own preference field, they may influence their friends' preference in their expert field when considering the social interest on their friends' item collecting. In order to model this social interest, in this paper, we proposed a simple method to compute users' social interest on the specific items in the recommender systems, and then integrate this social interest with similarity preference. The experimental results on two real-world datasetsEpinionsandFriendfeedshow that this method can significantly improve not only the algorithmic precision-accuracy but also the diversity-accuracy.

Suggested Citation

  • Da-Cheng Nie & Ming-Jing Ding & Yan Fu & Jun-Lin Zhou & Zi-Ke Zhang, 2013. "Social Interest For User Selecting Items In Recommender Systems," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(04), pages 1-11.
    • Handle: RePEc:wsi:ijmpcx:v:24:y:2013:i:04:n:s0129183113500228
    DOI: 10.1142/S0129183113500228
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0129183113500228
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0129183113500228?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. Guo, Xin-Yu & Guo, Qiang & Li, Ren-De & Liu, Jian-Guo, 2018. "Long-term memory of rating behaviors for the online trust formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 254-264.

    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:24:y:2013:i:04:n:s0129183113500228. 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.