Towards the Privacy-Preserving of Online Recommender System in Collaborative Learning Environment
Qing Tang, Marie‐Hélène Abel, Elsa Negre · 2020
To improve the performance of recommender system, more and more learner's attributes (e.g. learning style, learning ability, knowledge level, etc.) have been considered. But it has also triggered widespread privacy concerns due to their reliance on learner's personal information. Therefore, towards the privacy-preserving of online recommender system in collaborative learning environment, we propose a personalized recommender system with three customized settings about recording (full-collecting mode, semi-privacy mode, full-privacy mode) to collect learner's history study activities. We aim at extracting learner information from these activity records to build recommender system, which can not only make effective personalized recommendations but also meet privacy-preserving requirements.