Bridging Datasets and Hyperparameters: GCN-Based Link Prediction for Recommendation

Hyperparameter recommendation through meta-learning (HPR-MtL) has proven effective in a wide range of studies. At its core, HPR-MtL constructs a recommendation model using metadata extracted from historical learning tasks, such as dataset characteristics and the empirical performance of hyperparameter configurations. Existing approaches-typically based on k-nearest neighbors (KNN), linear regression, or collaborative filtering-focus primarily on modeling interactions between datasets and hyperpa