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IJIET 2015 Vol.5(10): 754-757 ISSN: 2010-3689
DOI: 10.7763/IJIET.2015.V5.605

A Comparison between Item-Based and Tag-Based Recommendation on a Knowledge Management System: A Preliminary Investigation

Worasit Choochaiwattana

Abstract—Knowledge is one of the most important critical factor for the success of organizations. As huge collections of knowledge are stored in a knowledge management system, hence the development of an automated knowledge dissemination mechanism becomes a challenging task. To improve performance of such a system, a “knowledge recommendation service” should be embedded. This paper compared the effectiveness of two recommendation techniques namely, an item-based recommendation (a collaborative filtering technique) and a tag-based recommendation (a content-based filtering technique). In order to evaluate the performance of the two proposed recommendations, a group of knowledge management system users were ask to be participants in this research study. The percentage of accuracy in knowledge items recommendations was used as an evaluation metric. The result of the experiment revealed that the tag-based recommendation had an accuracy of 86.67%, which is higher than that of item-based recommendation. Thus, the tag-based recommendation showed a promising approach to the task of automated knowledge dissemination in a knowledge management system.

Index Terms—Collaborative filtering, content based filtering, item-based recommendation, tag-based recommendation, knowledge management system.

Worasit Choochaiwattana is with the Search Engines and Intelligent Information Systems Research Laboratory, Faculty of Information Technology, Dhurakij Pundit University, Bangkok, Thailand (e-mail: worasit.cha@dpu.ac.th).


Cite: Worasit Choochaiwattana, "A Comparison between Item-Based and Tag-Based Recommendation on a Knowledge Management System: A Preliminary Investigation," International Journal of Information and Education Technology vol. 5, no. 10, pp. 754-757, 2015.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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