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

Yao, Dunhong; Li, Shijun; Li, Ang; Chen, Yu

doi:10.32604/cmc.2020.010186

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Factorizer. Factorization machine [22] is a general factor model based on matrix factorization proposed by Steffen Rendle. It belongs to the hidden factor model [23], which uses the interaction between different feature vectors to model, predicts the user's score on items, and then recommends them.…”

Section: Technologymentioning

confidence: 99%

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

Wang

2022

Scientific Programming

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With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs. With the increase of the number of courses, it is difficult for learners to find the courses they want quickly and accurately; that is, they encounter the problems of information overload and cognitive maze. Therefore, how to recommend personalized courses for learners according to their preferences has become one of the important problems that need to be solved urgently to improve the service quality of courses in online learning platforms. Therefore, in order to improve the accuracy of course recommendation, it is necessary to build an accurate and complete learner model. In order to improve the application effect of recommendation, this paper focuses on the recommendation method of emotional factors to improve the recommendation efficiency of learning resources. The traditional recommendation model is a method based on the user’s purchase behavior and historical information. However, in the emotional factors, the effect of traditional recommendation is limited. This paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. The experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation.

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Section: Technologymentioning

confidence: 99%

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

Wang

2022

Scientific Programming

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“…e expansion in the scale of schools, colleges, and universities creates a deep concern regarding the quality of education at the institutions and the mechanisms to improve it [1][2][3]. From the perspective of talent cultivation in colleges and universities, classroom teaching is the central link of the whole teaching work [4][5][6][7]. Its quality determines the overall quality of education in colleges and universities to a great extent.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks

Jin

Yang

et al. 2021

Mobile Information Systems

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Classroom teaching quality evaluation system can enable the school’s functional departments to accurately assess the performance of the teaching staff and current teaching operations. As per the requirements for cultivating high-quality talents, planned teaching staff construction and teaching reforms need to be carried out to promote teachers’ appointments. Improving the system makes the appointment process more scientific by giving due attention to the individual characteristics of all types of teachers while hiring them for related jobs. The system motivates the love of teaching, high academic level, high teaching level, and competitive teaching. In recent years, the rapid development of artificial intelligence and deep learning caused many colleges and universities to put forward the target of campus digitization and education informatization. The state of the classroom is a critical reference factor throughout the teaching and learning process for evaluating students’ acceptance of the course and the quality of the teaching. However, at present, the analysis of the classroom status is mainly conducted manually, which distracts teachers and is also not much precise. Therefore, finding a method that can improve the efficiency of classroom status analysis has great research significance. This study uses the deep neural network method to read each class’s video recording and analyze it from the aspects of students’ behavior and attendance. The system can realize class behavior and eventually evaluate the course quality employed to motivate teachers to improve teaching and overall quality of education.

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

Cited by 2 publications

References 12 publications

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks

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