Personalized recommender system for digital libraries

被引：9
作者：
Omisore, M.O. [1 ]
Samuel, O.W. [1 ]
机构：
[1] Department of Computer Science, Federal University of Technology Akure, Akure, Ondo
来源：
International Journal of Web-Based Learning and Teaching Technologies | 2014年 / 9卷 / 01期
关键词：
Cold start problem; Content-based filtering; Digital library; E-learning system; Fuzzy logic; Information retrieval; Paired samples test; Recommender system;
D O I：
10.4018/ijwltt.2014010102
中图分类号：
学科分类号：
摘要：
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 fltering (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.
引用
收藏
页码：18 / 32
页数：14
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