Design of innovation and entrepreneurial repository system based on personalized recommendations
- 33 Downloads
In order to promote the rational management and effective reuse of innovative project resources, a system of university students’ innovation and entrepreneurship resources based on personalized recommendation is designed and implemented. The advantages and disadvantages of traditional repository are studied. The characteristics of college students’ innovation and entrepreneurship project are analyzed. The system introduces the recommendation system in e-commerce, which can improve the efficiency of resource transmission to a certain extent. In the process of system implementation, the theoretical knowledge of Spring, Struts, Hibernate, Linux, Tomcat is applied to project practice. In addition, the current repository of retrieval methods and storage methods are studied. Based on the characteristics of the resources of innovation and entrepreneurship projects, the database was searched using the retrieval methods based on keyword retrieval, project retrieval, project-based and full-text retrieval. Based on the behavioral characteristics and attribute characteristics of college students’ innovation and entrepreneurship resource base, the collaborative filtering algorithm based on the project is selected. The results show that the system realizes the rational storage and scientific management of entrepreneurial resources. The diversity of resource queries has been implemented. It makes a good interaction between the resource base system and the learner.
KeywordsInnovation and entrepreneurship project Resource base Personalized recommendation Collaborative filtering
- 6.Jiang, S., Qian, X., Shen, J., et al.: Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans. Multimed. 17(6), 907–918 (2015)Google Scholar
- 7.Liu, J., Wang, Y., Yan, F.: An improved collaborative filtering recommendation algorithm. Comput. Mod. 32(9), 194–198 (2017)Google Scholar
- 8.Ma, Y., Chen, G., Wei, Q.: Finding users preferences from large-scale online reviews for personalized recommendation. Electron. Commer. Res. 17(1), 1–27 (2016)Google Scholar
- 12.Liu, Y., Cheng, J., Yan, C., et al.: Research on the matthews correlation coefficients metrics of personalized recommendation algorithm evaluation. J. Higher Educ. 37(6), 39–41 (2015)Google Scholar