Ensuring Novelty and Transparency in Learning Resource-Recommendation Based on Deep Learning Techniques

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.