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PCRS: Personalized Course Recommender System Based on Hybrid Approach
Z. Gulzar, , G. Deepak
Published in Elsevier B.V.
2018
Volume: 125
   
Pages: 518 - 524
Abstract
The traditional system of selecting courses to carry out research work is time consuming, risky and a tedious task, that not only badly affect the performance but the learning experience of a researcher as well. Therefore, choosing appropriate courses in seminal years could help to do research in a better way. This Study presents a recommender system that will suggest and guide a learner in selecting the courses as per their requirement. The Hybrid methodology has been used along with ontology to retrieve useful information and make accurate recommendations. Such an approach may be helpful to learners to increase their performance and improve their satisfaction level as well. The proposed recommender systems would perform better by mitigating the weakness of basic individual recommender systems. © 2018 The Authors. Published by Elsevier B.V.
About the journal
JournalData powered by TypesetProcedia Computer Science
PublisherData powered by TypesetElsevier B.V.
ISSN18770509