Beteiligte: | , |
---|---|
In: | International Journal of Web-Based Learning and Teaching Technologies, 9, 2014, 1, S. 18-32 |
veröffentlicht: |
IGI Global
|
Medientyp: | Artikel, E-Artikel |
Umfang: | 18-32 |
---|---|
ISSN: |
1548-1093
1548-1107 |
DOI: | 10.4018/ijwltt.2014010102 |
veröffentlicht in: | International Journal of Web-Based Learning and Teaching Technologies |
Sprache: | Ndonga |
Schlagwörter: | |
Kollektion: | IGI Global (CrossRef) |
<p>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.</p> |