IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7813457.html
   My bibliography  Save this article

Personalized News Recommendation Algorithm for Event Network

Author

Listed:
  • Yufan Han
  • Baiyuan Ding

Abstract

In order to improve the level of personalized news recommendation efficiently and accurately, an event network-oriented personalized news recommendation algorithm is proposed. First, the event network is used to analyze, predict users’ interests and preferences, and actively push information content to meet users’ personalized needs, so as to build a personalized news recommendation model. Under the mobile Internet technology, combined with the characteristics of the Internet, through the position and title similarity of sentences in the document and other features, the combined features are formed to calculate the sentence weight. Finally, the sentences are extracted according to the weight ranking to generate the news summary, so as to realize the research on personalized news recommendation algorithm for event network. The experimental results show that the proposed algorithm has high recall and coverage, short time, good recommendation effect, and strong recommendation performance.

Suggested Citation

  • Yufan Han & Baiyuan Ding, 2022. "Personalized News Recommendation Algorithm for Event Network," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, August.
    • Handle: RePEc:hin:jnlmpe:7813457
    DOI: 10.1155/2022/7813457
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/7813457.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/7813457.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/7813457?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
    ---><---

    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:hin:jnlmpe:7813457. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.