[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/igg/jiit00/v15y2019i4p41-56.html
   My bibliography  Save this article

Semantic Web mining for Content-Based Online Shopping Recommender Systems

Author

Listed:
  • Ibukun Tolulope Afolabi

    (Covenant University, Ota, Nigeria)

  • Opeyemi Samuel Makinde

    (Covenant University, Ota, Nigeria)

  • Olufunke Oyejoke Oladipupo

    (Covenant University, Ota, Nigeria)

Abstract
Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

Suggested Citation

  • Ibukun Tolulope Afolabi & Opeyemi Samuel Makinde & Olufunke Oyejoke Oladipupo, 2019. "Semantic Web mining for Content-Based Online Shopping Recommender Systems," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 15(4), pages 41-56, October.
    • Handle: RePEc:igg:jiit00:v:15:y:2019:i:4:p:41-56
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.2019100103
    Download Restriction: no
    ---><---

    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:igg:jiit00:v:15:y:2019:i:4:p:41-56. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.