PCRS: Personalized Course Recommender System Based on Hybrid Approach

Title

PCRS: Personalized Course Recommender System Based on Hybrid Approach

Creator

Gulzar Z.; Leema A.A.; Deepak G.

Description

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.

Source

Procedia Computer Science, Vol-125, pp. 518-524.

Date

2018-01-01

Publisher

Elsevier B.V.

Subject

E-learning; Knowledge Modelling; Ontology; Personalization; Recommendation

Coverage

Gulzar Z., BSAR Crescent University, Chennai, India; Leema A.A., BSAR Crescent University, Chennai, India; Deepak G., Christ Univerisy, Bangalore, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 18770509

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1016/j.procs.2017.12.067

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040664066&doi=10.1016%2fj.procs.2017.12.067&partnerID=40&md5=109e1b40fdd856ca31b0f31aa3c04b02