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Personalized Recommender System for Digital Libraries
ARTICLE

, , 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 .

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