PS-LDA: A course item model for tutorial personalized recommendation

Abstract

With the development of educational big data, personalized tutoring has become an important research direction to help people find interesting learning resources. However, due to limitation of learning resources, especially for the resource in unfamiliar subject areas, it may bring data sparseness of users’ learning matrix. In this paper, we propose PS-LDA, a potential probability generation model for course item on learning preferences and subject area aware. By considering the mix of these two factors, our model provides personalized guidance for designated users. Moreover, we present a top-k method for online recommendation by matching the results from P-LDA and S-LDA. Finally, the experiments on two real-life datasets can verify the effectiveness and efficiency of our model.