Design of dynamic career path recommendation system based on Markov decision process

Career path recommendation systems are mostly static recommendations for a certain stage, and it is difficult to dynamically adjust and update them based on factors such as time, environmental changes, and improvement of personal abilities. To achieve dynamic career path recommendation, this article constructs a dynamic career path recommendation system using Markov decision process. Firstly, by comprehensively collecting user data (including professional background, skills, interests, and market trends), the user's state space and action space are defined; secondly, by using logistic regression to calculate historical data and market trends, the transition probabilities of different career decisions can be estimated; then, considering factors such as salary growth, career development opportunities, career satisfaction, learning costs, and risks, the Analytic Hierarchy Process is used to assign appropriate weights to each parameter, and a comprehensive reward function is designed to quantitatively evaluate different decisions; finally, the value iterative optimization algorithm is used to calculate and recommend the optimal career path. The research results show that the average processing time of the system for each data point is 0.3155 seconds, and the average throughput is 3.0170 data points/second, indicating that the overall performance of the system is good. The mean square error of the test set is 0.0012, indicating that the predictive ability of the model is relatively stable. Compared to existing career path recommendation systems, this system can provide users with career planning recommendations that adapt to the constantly changing market environment through dynamic adjustment and optimization.