Dr. Guang Wang has a paper accepted by KDD 2025
Dr. Guang Wang, an Assistant Professor in the Computer Science Department, and his recent research has been published by The 2025 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2025) Applied Data Science track. The acceptance rate is 22%. This research paper is titled “Scalable Area Difficulty Assessment with Knowledge-enhanced AI for Nationwide Logistics Systems”. Dr. Guang Wang is the corresponding author of this paper.
In this work, they design RAICA (Ranking-Aggregated Isotonic Calibration Assessment) framework, which includes two key modules: (i) a Judgment Rank Aggregation module, which aggregates individual workers’ judgment rankings collected from surveys into an overall ranking to mitigate personal biases and inconsistency between different workers; (ii) an Isotonic Calibration module, which calibrates the assessment from existing machine learning models with the aggregated ranking through Isotonic regression to enhance the accuracy of area difficulty assessment with theoretical guarantees. Extensive evaluation based on real-world data shows that RAICA outperforms existing methods, increasing F1 score by 0.25.
The paper was presented at KDD 2025 in August.