Debiased magnitude-preserving ranking: Learning rate and bias characterization

Abstract Magnitude-preserving ranking (MPRank) under Tikhonov regularization framework has shown competitive performance on information retrial besides theoretical advantages for computation feasibility and statistical guarantees. In this paper, we further characterize the learning rate and asymptotic bias of MPRank, and then propose a new debiased ranking algorithm. In terms of the operator representation and approximation techniques, we establish their convergence rates and bias characterizations. These theoretical results demonstrate that the new model has smaller asymptotic bias than MPRank, and can achieve the satisfactory convergence rate under appropriate conditions. In addition, some empirical examples are provided to verify the effectiveness of our debiased strategy.