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Personalized Recommender System for Digital Libraries
ARTICLE
Omisore M. O., Samuel O. W., Department of Computer Science, Federal University of Technology Akure, Akure, Ondo, Nigeria
IJWLTT Volume 9, Number 1, ISSN 1548-1093 Publisher: IGI Global
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.
Citation
M. O., O. & O. W., S. (2014). Personalized Recommender System for Digital Libraries. International Journal of Web-Based Learning and Teaching Technologies, 9(1), 18-32. IGI Global. Retrieved August 11, 2024 from https://www.learntechlib.org/p/187116/.
Keywords
References
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