Personalized Recommender System for Digital Libraries

Omisore M. O. and Samuel O. W.
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Omisore M. O.: Department of Computer Science, Federal University of Technology Akure, Akure, Ondo, Nigeria
Samuel O. W.: Department of Computer Science, Federal University of Technology Akure, Akure, Ondo, Nigeria

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2014, vol. 9, issue 1, 18-32

Abstract: The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that suit their reading abilities was developed. Content-based filtering (CBF) was used to analyze learners' reading abilities while books that are found suitable to learners are recommended with fuzzy matching techniques. The yokefellow cold-start problem inherent to CBF is assuaged by cold start engine. An experimental study was carried out on a database of 10000 books from different categories of computing studies. The outcome tracked over a period of eight months shows that the proposed system induces greater user satisfaction and this attests users' desirability of the system.

Date: 2014
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