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 hyperparameters, i.e., performance observations. However, they often overlook important interactions within datasets or within hyperparameter configurations, such as similarity relationships, which are critical components embedded in the metadata. To overcome this limitation, we propose a novel hyperparameter recommendation framework formulated as a link prediction problem on a bipartite graph. In our approach, each historical dataset and each hyperparameter configuration is represented as a node, and the links between them are weighted by the observed performance. When a new dataset becomes available, it is added as a node, and the model predicts its potential links to hyperparameter nodes. To enhance prediction accuracy, we introduce a graph convolutional network (GCN) that simultaneously captures both homogeneous interactions (within datasets and within hyperparameters) and heterogeneous interactions (between datasets and hyperparameters)-a capability not found in existing methods. We evaluated our method on two popular deep learning models, ResNet and Vision Transformer (ViT), across 105 real-world classification datasets. Extensive experiments using multiple evaluation metrics demonstrate the superior performance of our approach compared to state-of-the-art hyperparameter recommendation baselines.