Authors: Choochart Haruechaiyasak, Mei-Ling Shyu, Shu-Ching Chen
Addresses: Information Research and Development Division (RD-I), National Electronics and Computer Technology Center (NECTEC), Thailand Science Park, Pathumthani 12120, Thailand. ' Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124 0640, USA. ' Distributed Multimedia Information System Laboratory, School of Computer Science, Florida International University, Miami, FL 33199, USA
Abstract: Traditional search engines require users to form the keyword based query which can accurately depict the search topic. More importantly, search engines are generally unable to customise the results according to the users| preferences. Recently, an alternative approach of retrieving the information, known as the recommender system is proposed. A recommender system is an intermediary program that intelligently generates a list of information which matches the users| preferences. In this paper, a new recommender system framework based on data mining techniques and the Semantic Web concept is proposed. Two information filtering methods for providing the recommended information (i.e., content-based and collaborative filtering) are considered. Both filtering techniques are based on data mining algorithms which provide efficiency in handling large data sets. In addition, the Semantic Web concept, in which the information is given well-defined meaning, is incorporated into the framework in order to provide the users with semantically-enhanced information. To demonstrate the potential use of the proposed framework, a system prototype for recommending the University of Miami|s Web pages was implemented to enhance the performance of the traditional query-based information retrieval approach provided on the website.
Keywords: recommender systems; information filtering; user personalisation; data mining; semantic web.
International Journal of Computer Applications in Technology, 2006 Vol.27 No.4, pp.298 - 311
Published online: 08 Jan 2007 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article