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Unsupervised Machine Learning based recommendation of pedagogical resources

Brahim Batouche 1 Armelle Brun 1 Anne Boyer 1 
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : In E-learning context, we can recommend pedagogical re- sources to help learners. In this context, the recommender proposes the nearest resource(s) in term of similarity, where the scarcity of resources may a↵ects seriously the quality of predictions. To make accurate predic- tions we begin in determining the scarce resources in order to be taken into account with the recommendation process. To acheive this objective we use the unsupervised neural network I2GNG (Improved Incremental Growing Neural Gas).
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Contributor : Armelle Brun Connect in order to contact the contributor
Submitted on : Friday, January 23, 2015 - 1:52:44 PM
Last modification on : Saturday, October 16, 2021 - 11:26:08 AM


  • HAL Id : hal-01108735, version 1



Brahim Batouche, Armelle Brun, Anne Boyer. Unsupervised Machine Learning based recommendation of pedagogical resources. EC-TEL, Oct 2014, Graz, Austria. ⟨hal-01108735⟩



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