Open Access
ARTICLE
A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines
Dunhong Yao1, 2, 3, Shijun Li4, *, Ang Li5, Yu Chen6
1 School of Computer Science and Engineering, Huaihua University, Huaihua, 418000, China.
2 Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan
Province Universities, Huaihua, 418000, China.
3 Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan
Province, Huaihua, 418000, China.
4 School of Computer, Wuhan University, Wuhan, 430072, China.
5 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
6 School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000, China.
* Corresponding Author: Shijun Li. Email: .
Computers, Materials & Continua 2020, 64(3), 1959-1975. https://doi.org/10.32604/cmc.2020.010186
Received 15 February 2020; Accepted 03 May 2020; Issue published 30 June 2020
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.
Keywords
Cite This Article
D. Yao, S. Li, A. Li and Y. Chen, "A recommendation method for highly sparse dataset based on teaching recommendation factorization machines,"
Computers, Materials & Continua, vol. 64, no.3, pp. 1959–1975, 2020. https://doi.org/10.32604/cmc.2020.010186
Citations