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 user’s history as queries of keywords. The experimental results proved the system usefulness based on a conducted survey.
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Alkhatib, W., Araache, E., Rensing, C., & Schnitzer, S. (2018). Ensuring Novelty and Transparency in Learning Resource-Recommendation Based on Deep Learning Techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11082 LNCS, pp. 609–612). Springer Verlag. https://doi.org/10.1007/978-3-319-98572-5_56