Research Area:  Machine Learning
In this paper, we present an innovative approach for learning resources recommendation. The approach takes into account users short and long-term interests while ensuring transparency in explaining why a resource is recommended. Our approach relies on Deep Semantic Similarity Model (DSSM) to implicitly measure the semantic similarity between the user interest and the available resources for a recommendation. By taking into consideration the user previous activities, knowledge and current interest, the system reflects the users history as queries of keywords. The experimental results proved the system usefulness based on a conducted survey.
Keywords:  
Transparency
Learning
Resource-Recommendation
Deep Learning Techniques
Machine Learning
Deep Semantic Similarity Model (DSSM)
Author(s) Name:  Wael Alkhatib, Eid Araache, Christoph Rensing, Steffen Schnitzer
Journal name:  Computer Science
Conferrence name:  
Publisher name:  Springer
DOI:  https://doi.org/10.1007/978-3-319-98572-5_56
Volume Information:  
Paper Link:   https://link.springer.com/chapter/10.1007/978-3-319-98572-5_56