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Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios

Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios

M. Waseem Chughtai, Imran Ghani, Ali Selamat, Seung Ryul Jeong
Copyright: © 2014 |Volume: 4 |Issue: 1 |Pages: 14
ISSN: 2155-5605|EISSN: 2155-5613|EISBN13: 9781466657106|DOI: 10.4018/ijtem.2014010101
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MLA

Chughtai, M. Waseem, et al. "Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios." IJTEM vol.4, no.1 2014: pp.1-14. http://doi.org/10.4018/ijtem.2014010101

APA

Chughtai, M. W., Ghani, I., Selamat, A., & Jeong, S. R. (2014). Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios. International Journal of Technology and Educational Marketing (IJTEM), 4(1), 1-14. http://doi.org/10.4018/ijtem.2014010101

Chicago

Chughtai, M. Waseem, et al. "Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios," International Journal of Technology and Educational Marketing (IJTEM) 4, no.1: 1-14. http://doi.org/10.4018/ijtem.2014010101

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Abstract

Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.

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