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Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

Xueying Ma, Lu Ye
Copyright: © 2018 |Volume: 10 |Issue: 3 |Pages: 15
ISSN: 1941-8647|EISSN: 1941-8655|EISBN13: 9781522543725|DOI: 10.4018/IJMBL.2018070103
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MLA

Ma, Xueying, and Lu Ye. "Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan." IJMBL vol.10, no.3 2018: pp.23-37. http://doi.org/10.4018/IJMBL.2018070103

APA

Ma, X. & Ye, L. (2018). Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan. International Journal of Mobile and Blended Learning (IJMBL), 10(3), 23-37. http://doi.org/10.4018/IJMBL.2018070103

Chicago

Ma, Xueying, and Lu Ye. "Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan," International Journal of Mobile and Blended Learning (IJMBL) 10, no.3: 23-37. http://doi.org/10.4018/IJMBL.2018070103

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Abstract

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in e-learning systems and a few of them have seriously affected the learning goals of users. Thus, an intelligent e-learning system have been developed which can recommend professional and targeted courses according to their career goals. First, an enhanced collaborative filtering (CF) approach is proposed considering users' career goals and background information. Then, the relevance between career goals and courses are calculated to alleviate the cold-start problem and recommend specialized courses for users. Finally, a PrefixSpan algorithm is combined with the above methods to generate a personalized learning path step by step. Some experiments are carried out with real users of different professions to test the performance of the hybrid algorithm.

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