@Article{cmc.2020.010186,
AUTHOR = {Dunhong Yao, Shijun Li, Ang Li, Yu Chen},
TITLE = {A Recommendation Method for Highly Sparse Dataset Based on  Teaching Recommendation Factorization Machines},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1959--1975},
URL = {http://www.techscience.com/cmc/v64n3/39470},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2020.010186}
}