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Abstract

In order to solve the problem of low competitiveness of college students in the job market and high unemployment rate, a new joint gate graph neural network recommendation algorithm is proposed. In the experimental results, the proposed joint gated graph neural network recommendation algorithm shows excellent performance on multiple datasets. In four datasets of different sizes, the average recall rate of the algorithm is as high as 85%, and the average F1 value is 0.87, showing the ability to significantly outperform the traditional recommendation algorithm. In addition, when the size of the dataset increases, the performance of the algorithm also improves correspondingly, and the average values of NDCG and HR reach 0.59 and 0.55, respectively, indicating that the algorithm has superior performance in personalized recommendation. The experiment also shows that the optimal embedding dimension is 85, and the convergence performance of the algorithm stops changing when the number of iterations reaches 10. This paper proposes a new recommendation algorithm, which combines long- and short-term memory mechanism and two-layer attention grouping to solve the shortcomings of traditional recommendation systems in processing large amounts of user data and interest matching. The practical impact of the proposed approach is to more effectively match learners’ interests with course content, improve learning efficiency, and encourage students to improve their social skills and innovative thinking on their own, thus enhancing their employability competitiveness.

Keywords

gated attention group learning platforms innovation and entrepreneurship recommendation algorithms project courses

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Learning platform for university innovation and entrepreneurship projects based on the joint gated graph attention group recommendation algorithm

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