Course Recommendation Based on Query Classification Approach

Zameer Gulzar and A. Anny Leema
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Zameer Gulzar: Department of Computer Applications, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
A. Anny Leema: Department of Computer Applications, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2018, vol. 13, issue 3, 69-83

Abstract: This article describes how with a non-formal education, a scholar has to choose courses among various domains to meet the research aims. In spite of this, the availability of large number of courses, makes the process of selecting the appropriate course a tedious, time-consuming, and risky decision, and the course selection will directly affect the performance of a scholar. The best approach to solve such problems and to produce desirable results is to use a “recommendation system.” Recommender systems at the core employ information retrieval techniques and the ongoing effort of such information retrieval systems is to deliver the most relevant information to the learner. Therefore, if a recommender system is able to recognize the intent and requirements that a user express in the form of queries, it can generate more valid recommendations. This article presents an N-Gram classification technique which can be used to generate course recommendations to scholars depend on the requirements and domain of interest. This way of personalization can improve the quality of research and learning experience by recommending courses which are otherwise overlooked by scholars, as it takes the time to go through the curriculum and finding the best possible match.

Date: 2018
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