A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

摘要

There is no reasonable scientific basis for selecting the excellent teachers of the school's courses. To solve the practical problem, we firstly give a series of normalization models for defining the key attributes of teachers' professional foundation, course difficulty coefficient, and comprehensive evaluation of teaching. Then, we define a partial weight function to calculate the key attributes, and obtain the partial recommendation values. Next, we construct a highly sparse Teaching Recommendation Factorization Machines (TRFMs) model, which takes the 5-tuples relation including teacher, course, teachers' professional foundation, course difficulty, teaching evaluation as the feature vector, and take partial recommendation value as the recommendation label. Finally, we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse dataset. Experimental results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching dataset. The recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse datasets. The proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools, which can effectively improve the teaching quality.