Application and Research of Intelligent Recommendation Algorithm in Human-Computer Interaction Design in Electric Power Industry Digital Transformation

In the context of the digital transformation of the power industry, intelligent recommendation algorithms play an increasingly prominent role in improving human-computer interaction design. This research focuses on the exploration and practice of intelligent recommendation algorithm, especially random forest algorithm and ranking learning algorithm, in the power industry. In this paper, an intelligent recommendation model based on user behavior data is designed, which combines random forest algorithm and ranking learning method to optimize the user interface of power system. Through dynamic analysis of user behavior data, the model provides personalized recommendation content, and effectively improves the efficiency and satisfaction of user operation. In the system simulation part, a comprehensive evaluation model is constructed to simulate the performance of the algorithm under different operating scenarios. The simulation results show that the designed intelligent recommendation algorithm is superior to the traditional methods in terms of response speed and recommendation accuracy, especially in complex interactive scenarios. Through accurate data analysis, this paper verifies the applicability and robustness of the algorithm in different user groups.