IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0111005.html
   My bibliography  Save this article

Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering

Author

Listed:
  • Wei Zeng
  • An Zeng
  • Hao Liu
  • Ming-Sheng Shang
  • Yi-Cheng Zhang

Abstract

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0111005
    DOI: 10.1371/journal.pone.0111005
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111005
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0111005&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0111005?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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.
    2. Wang, Ximeng & Liu, Yun & Xiong, Fei, 2016. "Improved personalized recommendation based on a similarity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 271-280.

    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. 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.
    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.

    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:plo:pone00:0111005. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.