Design and Implementation of an Optimized Career Planning System for College Students Using a Hybrid Dijkstra-Genetic Algorithm

Abstract

Student career scheduling is divided into regular scheduling and dynamic optimal scheduling. Regular scheduling is the planning task of calculating a student's career year, and the reference parameters are some student career data. When facing the complex career problems of college students, achieving the expected scheduling tasks is difficult. Aiming at the problems existing in college students' career planning, this paper effectively combined the Dijkstra and genetic algorithms to obtain the D-GA optimization algorithm and apply it in the scheduling scheme. The experimental outcomes indicate that the graduate job recommendation algorithm introduced in this study achieves the highest performance, with a hit rate of 44.37% when K=50. This is approximately double the effectiveness of the CBF approach and around 20% higher than the neighborhood-based CF method. The mean reciprocal rank was 17.14%, which is nearly seven times greater than that of the CBF technique and about 3% better than the neighborhoodbased CF model. The data problem framework aligns with real-world conditions and is developed based on relevant aspects of college students' career planning. According to the advantages and disadvantages of the Dijkstra algorithm and genetic algorithm, combined with students' career problems, the Dijkstra algorithm was improved and combined with the genetic algorithm to form the D-GA algorithm and applied to the solution optimization process. Finally, combined with J2EE technology, college students' career planning system was realized.