A Novel Precise Personalized Learning Recommendation Model Regularized with Trust and Influence

Abstract

Many precise personalized learning recommendations in massive open online courses (MOOCs) have emerged in the intelligence education field. Up to now, most researches simply put the dual learner-resources relations into consideration and are short of studies looking deep into its intrinsic social relation, thus rarely introducing the influential factors such as social trust, which means to apply the mutual trust relation between learners in the precise personalized learning recommendation. Therefore, we propose a personalized learning recommendation method based on learners’ trust and conduct a quantitative analysis on two aspects: social trust and influence, so as to realize a precise personalized learning recommendation service. First, we establish a new module on social trust scale which integrates the interactive information and preference degree to reveal the implicit trust relation between learners in social networks and construct social trust networks. Next, we adopt improved structural hole (ISH) algorithm by integrating the topological structure of social trust network with learners’ interactive information and identify the most influential learners cluster by the ISH algorithm. For the final stage, we predict the score of target learners based on explicit and implicit feedback information and realize the personalized learning recommendation for new learners. Since the score is predicted, we compare MAE and RMSE in two real-world datasets which are Canvas Network and Wanke website, respectively. The result of experiment validates the accuracy and effectiveness of our recommendation model.