IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach

Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach
View Sample PDF
Author(s): Zameer Gulzar (B.S.A.R Crescent Institute of Science and Technology, Chennai, India), L. Arun Raj (B.S.A.R Crescent Institute of Science and Technology, Chennai, India)and A. Anny Leema (School of Computer Science and Engineering VIT, Vellore, India)
Copyright: 2019
Volume: 15
Issue: 2
Pages: 23
Source title: International Journal of Information and Communication Technology Education (IJICTE)
Editor(s)-in-Chief: David D. Carbonara (Duquesne University, USA)
DOI: 10.4018/IJICTE.2019040106

Purchase

View Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach on the publisher's website for pricing and purchasing information.

Abstract

Traditional e-learning systems lack the personalization feature to guide learners for selecting the most suitable courses needed. Choosing appropriate courses in the seminal years is important for a future learner who depends on such decisions, as selecting the wrong courses means a mismatch between learner's capability and personal interests. Therefore, a recommender system was developed to suggest and direct the students in selecting the appropriate courses. This study presents algorithms to personalize courses for scholars based on their interests to make learning effective and more productive. The hybrid methodology has been used to retrieve useful information and make accurate recommendations to help learners to increase their performance and improve their satisfaction level. The results suggest that a hybrid approach is better as it will enjoy all the advantages of the individual recommender systems and mitigate their limitations. A threshold-based nearest neighborhood approach will further strengthen the proposed system by finding a similar learner for targeted learners.

Related Content

Xinhua Wang, Yue Zheng, Lei Wu. © 2024. 18 pages.
Mohammed Abdullatif Almulla. © 2024. 26 pages.
Ahmed Abdulateef Al Khateeb, Tahani I. Aldosemani, Sumayah Abu-Dawood, Sameera Algarni. © 2024. 16 pages.
XiFeng Liao. © 2024. 19 pages.
Min Zhang. © 2024. 17 pages.
Shaobin Chen, Qingrong Li, Tao Wang. © 2024. 22 pages.
Kyosuke Takami, Brendan Flanagan, Yiling Dai, Hiroaki Ogata. © 2024. 23 pages.
Body Bottom