IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9617410.html
   My bibliography  Save this article

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Author

Listed:
  • Ruihui Mu
  • Xiaoqin Zeng

Abstract

To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.

Suggested Citation

  • Ruihui Mu & Xiaoqin Zeng, 2018. "Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, July.
    • Handle: RePEc:hin:jnlmpe:9617410
    DOI: 10.1155/2018/9617410
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2018/9617410.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2018/9617410.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/9617410?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:9617410. 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.