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<p>In this paper, firstly, the average prediction rating of interest points is performed by a recommendation model incorporating multiple factors through probabilistic matrix decomposition to improve the accuracy of the beauty teaching features obtained by matrix decomposition. Then, we combine the collaborative filtering recommendation algorithm and propose a recommendation model called TGSS-MF, and optimize the TGSS-MF recommendation model through the model of neural network for the sparse data problem faced by the interest point recommendation and the hidden feature vector representation problem of users and interest points, and finally use the TGSS-MF recommendation model to analyze the user needs of teachers, students and system administrators who are involved in teaching and learning. Finally, the TGSS-MF recommendation model is used to analyze the needs of users such as teachers, students, and system administrators involved in teaching and learning. A mobile teaching platform is designed to meet the characteristics of American voice teaching in colleges and universities. The performance analysis of the TGSS-MF recommendation model shows that when k=10, the accuracy and recall of the TGSS-MF model in the two data sets are 0.095 and 0.113, respectively, which are better than the other three algorithms in both accuracy and recall. This study can present more rich resources to students through modern Internet technology, which can help students learn effectiveness.</p>
</abstract>

In this paper, firstly, the average prediction rating of interest points is performed by a recommendation model incorporating multiple factors through probabilistic matrix decomposition to improve the accuracy of the beauty teaching features obtained by matrix decomposition. Then, we combine the collaborative filtering recommendation algorithm and propose a recommendation model called TGSS-MF, and optimize the TGSS-MF recommendation model through the model of neural network for the sparse data problem faced by the interest point recommendation and the hidden feature vector representation problem of users and interest points, and finally use the TGSS-MF recommendation model to analyze the user needs of teachers, students and system administrators who are involved in teaching and learning. Finally, the TGSS-MF recommendation model is used to analyze the needs of users such as teachers, students, and system administrators involved in teaching and learning. A mobile teaching platform is designed to meet the characteristics of American voice teaching in colleges and universities. The performance analysis of the TGSS-MF recommendation model shows that when k=10, the accuracy and recall of the TGSS-MF model in the two data sets are 0.095 and 0.113, respectively, which are better than the other three algorithms in both accuracy and recall. This study can present more rich resources to students through modern Internet technology, which can help students learn effectiveness.

The impact of modern Internet technology on the teaching of American voice in colleges and universities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, firstly, the average prediction rating of interest points is performed by a recommendation model incorporating multiple factors through...

The impact of modern Internet technology on the teaching of American voice in colleges and universities

Online veröffentlicht: 09. Sept. 2023

Seitenbereich: -

Eingereicht: 11. Okt. 2022

Akzeptiert: 20. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00322

Schlüsselwörter
Internet technology, Probability matrix, Neural network, TGSS-MF recommendation model, Beautiful voice teaching platform

© 2023 Tie Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The impact of modern Internet technology on the teaching of American voice in colleges and universities

Online veröffentlicht: 09. Sept. 2023

Seitenbereich: -

Eingereicht: 11. Okt. 2022

Akzeptiert: 20. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00322

SchlüsselwörterInternet technology, Probability matrix, Neural network, TGSS-MF recommendation model, Beautiful voice teaching platform

© 2023 Tie Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Internet technology, Probability matrix, Neural network, TGSS-MF recommendation model, Beautiful voice teaching platform