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

Yao, Dunhong; Deng, Xinyang

doi:10.1109/access.2020.3039011

Cited by 8 publications

(4 citation statements)

References 73 publications

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“…To enhance the personalization and efficiency of recommendation, a course instructor recommendation system (FCTR-LFM) depended on fuzzy clustering and latent factor model (LFM) was developed by Yao and Deng [12]. The major task consists of defining a series of ways to achieve quantitative results of teacher characteristics, course features, and teaching performance under the guidance of pedagogy standards.…”

Section: Literature Surveymentioning

confidence: 99%

Enriching Song Recommendation Through Facial Expression Using Deep Learning

Deore¹

2023

ISI

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The music recommendation systems are highly linked with the emotional response of the user as the majority of the music is based on the mood of the listener. A large number of researches have been performed for the detection of emotion through the use of a variety of different techniques. These approaches have been helpful in achieving the emotion of the subject using various devices and other hardware which can be highly expensive with very low rates of accuracy. Whereas the detection of expression of the subject can be useful in determining the mood or the emotion with a considerable degree of accuracy. Therefore, to achieve the effective identification of emotion of an individual for effective music recommendation has been proposed in this research paper. The presented approach utilizes image normalization and Convolutional Neural Networks (CNN) which are trained on a dataset consisting of a number of different emotional responses. This trained model is then used to determine the mood of the individual and recommend music based on the detected mood. The experimental evaluation of the approach is performed to determine the accuracy of the emotion recognition which has resulted in highly accurate results. We achieved 62.88% testing accuracy with MSE and RMSE values of 8.5 and 2.9 respectively. The obtained results are promising and show that the fuzzy classification technique optimizes the outcomes.

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Section: Literature Surveymentioning

confidence: 99%

Enriching Song Recommendation Through Facial Expression Using Deep Learning

Deore¹

2023

ISI

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“…Currently, there are more than 3.3 million rural teachers in China who shoulder the mission of educating hundreds of millions of rural students and, more importantly, the responsibility of improving the quality of the rural population [1][2][3]. The Party and the state have always attached great importance to the construction of rural teachers and have adopted a series of policies and initiatives in stabilizing and expanding the scale, improving the level of treatment, and strengthening the training, etc., which has led to a great change in the face of the rural teachers' team, and a significant improvement in the quality of education in the countryside [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Regression analysis of the effect of social support on rural teachers’ willingness to stay in the profession

Du,

Sun,

2024

Applied Mathematics and Nonlinear Sciences

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The retention of rural teachers is crucial not only for enhancing the quality of education in rural areas but also for the cultivation of talent and broader social development. This article conducts a detailed analysis of rural teachers’ willingness to remain in their positions by constructing and comparing various statistical models, including multi-level linear models and logistic regression models. Based on the results of model fitting, it specifically derives a logistic regression model that identifies the factors influencing rural teachers’ intentions to stay at the conclusion of their terms. Finally, 250 rural teachers as a survey research object, using the research method of this paper to analyze and explore. After the study, it is concluded that social support will have a significant positive impact on the willingness to stay when B = 0.121, 95% confidence interval is [0.298, 0.434], β = 0.532, t = 18.326, p < 0.001. Work pressure and working hours negatively predicted rural teachers’ willingness to stay in the profession, while social support positively predicted rural teachers’ desire to remain in the profession. The higher the level of social support, the higher the degree of recognition of rural teachers’ profession in the family, school, and society.

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“…Compared with other matrix factorization algorithms, FunkSVD algorithm is simpler and the algorithm complexity is lower, which is very suitable for processing big data. Therefore, this article uses the FunkSVD algorithm for big data recommendation [8].…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithm of Improved FunkSVD Based on GPU

Xiaochen

Liu

2022

IEEE Access

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Nowadays, the increasing amount of network data information and the development of big data technology have brought development opportunities and challenges to the recommendation system. The model-based collaborative filtering algorithm has become one of the mainstream algorithms in the recommendation system. For example, the Funk Singular Value Decomposition (FunkSVD) algorithm. However, in the face of big data calculations, data sparseness and iterative oscillations often affect the accuracy of the FunkSVD algorithm. Moreover, when the data volume is in units of GB or more, the FunkSVD algorithm runs slowly and is not effective. Therefore, we propose an improved FunkSVD algorithm (IFABG) based on RMSProp (Root Mean Square Prop) and GPU (Graphics Processing Unit) to solve this problem. Firstly, we use RMSProp algorithm to improve the traditional FunkSVD algorithm, alleviate data sparseness and iterative shock, and improve the prediction accuracy of the algorithm. Next, we implemented the parallelization of the improved FunkSVD algorithm in the GPU, which increased the calculation speed of the algorithm. Finally, we verify the IFABG algorithm under the Movielens dataset. The experimental results show that the IFABG algorithm is very suitable for processing sparse data, and it alleviates the iterative shock, and the prediction accuracy rate is about 30% higher than that of the traditional FunkSVD algorithm. The experimental results also show that the IFABG algorithm has a good acceleration effect. Under the same size data set, the IFABG algorithm is faster than the traditional FunkSVD, and the acceleration ratio can be as high as 19.27.

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

Cited by 8 publications

References 73 publications

Enriching Song Recommendation Through Facial Expression Using Deep Learning

Enriching Song Recommendation Through Facial Expression Using Deep Learning

Regression analysis of the effect of social support on rural teachers’ willingness to stay in the profession

Parallel Algorithm of Improved FunkSVD Based on GPU

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