Group-Sparse Inductive Matrix Completion Through Transfer Learning

The emergence of big data has enabled the creation of significant models by allowing the storage of large data volumes. Transfer learning is a machine learning technique that transfers knowledge between different domains by utilizing pretrained models from the source domain to optimize the target domain. In contrast, inductive matrix completion is a method that leverages side information from multiple sources to improve task performance. This paper explores inductive matrix completion within the transfer learning framework, with our proposed approach assuming group sparsity for the difference between the core matrices of the target and source domains. Theoretical guarantees of our method are investigated to demonstrate the gains achieved through transfer learning compared with standard inductive matrix completion. Several synthetic experiments are conducted to evaluate the performance of the proposed approach and existing methods, demonstrating that our method outperforms others.