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|Author:||Wael Alkhatib, Eid Araache, Christoph Rensing, Steffen Schnitzer|
|Kind:||In proceedings - use for conference & workshop papers|
|Book title:||in the proceeding of The 13th European Conference on Technology Enhanced Learning.|
|Keywords:||content-based recommender system; word embeddings; deep learning; semantic relations; ontologies.|
|Research Area(s):||Knowledge Media|
|Abstract:||A signicant dierence in the requirements for recommender systems in the TeL area compared to commercial recommendations lies in the necessity to recommend not primarily similar, but also new items. This is a great challenge for recommender systems. Existing approaches from the family of knowledge-based recommender systems usually use manually created ontologies. In this paper, we present an innovative approach applying deep learning methods. 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. In accordance with existing approaches, our process also uses an ontology, but it is created automatically. The experimental results show that our system signicantly outperforms the baseline in terms of F1-measure and proved its usefulness based on a conducted survey.|
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