Teaching Teacher Recommendation Method Based on Fuzzy Clustering and Latent Factor Model

Abstract

Colleges and universities attach great importance to the quality of undergraduate teaching. To virtually guarantee the course's teaching quality, the key lies in recommending suitable teachers for the course scientifically. It is a seemingly simple but very complicated problem. Moreover, with the development of colleges and universities, new courses are continually set up, and new teachers are introduced, which further complicates the problem. The problem has not been solved well for many years. Therefore, we propose a course teacher recommendation model (FCTR-LFM) based on fuzzy clustering and the latent factor model (LFM) to solve this problem. Firstly, under the guidance of pedagogy theories and methods, we conduct quantitative modeling for teachers and courses' relevant characteristics and combine the quantitative results with historical teaching scores to establish a large-scale sparse course teaching evaluation matrix as the recommendation dataset. Next, we adopt the improved fuzzy clustering model to realize teachers' automatic clustering according to their characteristics and use the teacher cluster to reconstruct the teaching evaluation matrix, significantly reducing the dataset's size and reducing the sparsity. Then, we used the improved LFM to predict the score items in the evaluation matrix, including the missing score items. Finally, the prediction evaluation scores are sorted according to the course, and the TOP-N recommendation of the course teachers is realized. The experimental results show that FCTR-LFM can realize the prediction and recommendation well using the optimized parameters. It effectively solves the problem that there is no scientific basis for recommending suitable teachers for the course for a long time.