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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 8/2019

23.02.2019

An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests

verfasst von: Sankar Pariserum Perumal, Ganapathy Sannasi, Kannan Arputharaj

Erschienen in: The Journal of Supercomputing | Ausgabe 8/2019

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Abstract

A relevant and suitable content recommendation is an important and challenging task in e-learning. Relevant terms are retrieved in a recommender system that should also cope with varying user preferences over time. This paper proposes a novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels. This is achieved by generating frequent item patterns after consolidating the user interest changes with an extended error margin quotient. Moreover, fuzzy rules are used in this work to enable the rule mining constraints for accommodating all types of learners while applying rules on the pattern tables. This method aims at mining the data stream preferences into equal-sized windows and caters to the varying user interest ratings over time. Experiments prove its efficiency and accuracy over existing conventional methods.
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Metadaten
Titel
An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests
verfasst von
Sankar Pariserum Perumal
Ganapathy Sannasi
Kannan Arputharaj
Publikationsdatum
23.02.2019
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 8/2019
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-019-02791-z

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