IDEAS home Printed from https://ideas.repec.org/a/igg/jtem00/v4y2014i1p1-14.html
   My bibliography  Save this article

Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios

Author

Listed:
  • M. Waseem Chughtai

    (Software Engineering Department, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor, Malaysia)

  • Imran Ghani

    (Software Engineering Department, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor, Malaysia)

  • Ali Selamat

    (Software Engineering Department, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor, Malaysia)

  • Seung Ryul Jeong

    (School of Business IT, Kookmin University, Seoul, South Korea)

Abstract

Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.

Suggested Citation

  • M. Waseem Chughtai & Imran Ghani & Ali Selamat & Seung Ryul Jeong, 2014. "Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios," International Journal of Technology and Educational Marketing (IJTEM), IGI Global, vol. 4(1), pages 1-14, January.
  • Handle: RePEc:igg:jtem00:v:4:y:2014:i:1:p:1-14
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijtem.2014010101
    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:jtem00:v:4:y:2014:i:1:p:1-14. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Journal Editor). General contact details of provider: https://www.igi-global.com .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.