Construction of an innovative entrepreneurship project learning platform introducing a group recommendation algorithm for college students

Abstract

This work aims to construct an innovative entrepreneurship project learning platform based on a group recommendation algorithm, which assists college students in better identifying and utilizing appropriate team resources during the entrepreneurial process. In order to achieve this goal, the genetic algorithm and probability models are introduced in the research process to improve the accuracy and personalization of group recommendations. First, the genetic algorithm is designed based on the initial population of individuals consisting of college students' diverse innovative entrepreneurship project information and personal characteristics. Individuals are iteratively optimized to find the optimal team combinations through crossover, mutation, and selection. Simultaneously, a probability model is employed to establish the group recommendation algorithm, and a large number of data samples and features are utilized for model training and prediction. Finally, the effectiveness of the proposed Genetic Algorithm-Probability Model-Group Recommendation (GAPMGR) is validated through experiments and comparative analysis. The research findings indicate that the GAPMGR method proposed has significantly improved team combination recommendations for college students' innovative entrepreneurship project learning platforms. Compared to traditional methods, the GAPMGR algorithm's group recommendation performance has improved by 4.92%, enabling more accurate matching of suitable team resources. By introducing the genetic algorithm and probability models, the innovative entrepreneurship project learning platform based on group recommendation can effectively enhance students' entrepreneurial abilities and team collaboration effects. This platform can provide personalized team resource recommendations for students, promote interdisciplinary cooperation, and facilitate the successful implementation of innovative entrepreneurship projects. In the fields of education and entrepreneurship, this work provides a valuable entrepreneurial learning tool for college students and serves as a reference for educational institutions and entrepreneurship mentors. In the field of technology, this work explores the application of the genetic algorithm and probability models in group recommendation, offering new ideas and methods for further research and application.