Ensuring Novelty and Transparency in Learning Resource-Recommendation Based on Deep Learning Techniques
| Autor: | Eid Araache, Wael Alkhatib, Steffen Schnitzer, Christoph Rensing |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Learning resource
Measure (data warehouse) Information retrieval business.industry Computer science Deep learning 05 social sciences Novelty Recommender system Resource (project management) Semantic similarity Transparency (graphic) 0502 economics and business 050211 marketing Artificial intelligence 0509 other social sciences 050904 information & library sciences business |
| Zdroj: | Lifelong Technology-Enhanced Learning ISBN: 9783319985718 EC-TEL |
| DOI: | 10.1007/978-3-319-98572-5_56 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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