Abstract
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: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_56
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DOI: https://doi.org/10.1007/978-3-319-98572-5_56
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