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<p>In this paper, the student documents are processed by experimental strategy, with the topic of college innovation and entrepreneurship resources recommendation, word diffusion and improved LDA topic model distribution training, set the training topic model and the number of topic top-words, and compare the high-frequency vocabulary and the probability distribution error of the 80% user coverage case and 90% user coverage case. Explore the stability of social media information pushing, the performance comparison results of both combine LDA based on user comment information text and LDA modeling directly using user comment information text as a reference. Select the experimental objects of innovation and entrepreneurship resources in colleges and universities, obtain the recommendation information of innovation and entrepreneurship resources in social media, and analyze the accuracy and satisfaction of algorithmic recommendation. Analyze the sharing characteristics of algorithmic recommendation information on social media and propose educational countermeasures against its adverse effects. According to the analysis, the improved LDA topic model results in a 15% and 85% increase in the number of same words in each topic when top_words is 10. When top_words is 20, the proportion of the same words in each theme is 15% and 78%, respectively. This indicates that the innovation and entrepreneurship education topic features are stable, and the accuracy and satisfaction of the algorithmic recommendation have improved.</p>
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In this paper, the student documents are processed by experimental strategy, with the topic of college innovation and entrepreneurship resources recommendation, word diffusion and improved LDA topic model distribution training, set the training topic model and the number of topic top-words, and compare the high-frequency vocabulary and the probability distribution error of the 80% user coverage case and 90% user coverage case. Explore the stability of social media information pushing, the performance comparison results of both combine LDA based on user comment information text and LDA modeling directly using user comment information text as a reference. Select the experimental objects of innovation and entrepreneurship resources in colleges and universities, obtain the recommendation information of innovation and entrepreneurship resources in social media, and analyze the accuracy and satisfaction of algorithmic recommendation. Analyze the sharing characteristics of algorithmic recommendation information on social media and propose educational countermeasures against its adverse effects. According to the analysis, the improved LDA topic model results in a 15% and 85% increase in the number of same words in each topic when top_words is 10. When top_words is 20, the proportion of the same words in each theme is 15% and 78%, respectively. This indicates that the innovation and entrepreneurship education topic features are stable, and the accuracy and satisfaction of the algorithmic recommendation have improved.

Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics 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, the student documents are processed by experimental strategy, with the topic of college innovation and entrepreneurship resources...

Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics in colleges and universities

Online veröffentlicht: 06. Dez. 2023

Seitenbereich: -

Eingereicht: 11. Jan. 2023

Akzeptiert: 16. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01378

Schlüsselwörter
LDA model, Algorithmic recommendation, Information sharing characteristics, Probability distribution error, Innovation and entrepreneurship education

© 2023 Wei Dai, published by Sciendo

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

Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics in colleges and universities

Online veröffentlicht: 06. Dez. 2023

Seitenbereich: -

Eingereicht: 11. Jan. 2023

Akzeptiert: 16. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01378

SchlüsselwörterLDA model, Algorithmic recommendation, Information sharing characteristics, Probability distribution error, Innovation and entrepreneurship education

© 2023 Wei Dai, published by Sciendo

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

Schlüsselwörter
LDA model, Algorithmic recommendation, Information sharing characteristics, Probability distribution error, Innovation and entrepreneurship education