An Admissible Shift-Consistent Method for Recommender Systems
This addresses fairness and robustness issues in recommender systems, though it appears incremental as it builds on existing mathematical frameworks.
The paper tackles matrix/tensor completion in recommender systems by proposing a shift-consistency constraint, which guarantees admissibility, fairness against malicious influence, and robustness through unique imputation.
In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems. Our method provably guarantees several key mathematical properties: (1) satisfies a recently established admissibility criterion for recommender systems; (2) satisfies a definition of fairness that eliminates a specific class of potential opportunities for users to maliciously influence system recommendations; and (3) offers robustness by exploiting provable uniqueness of missing-value imputation. We provide a rigorous mathematical description of the method, including its generalization from matrix to tensor form to permit representation and exploitation of complex structural relationships among sets of user and product attributes. We argue that our analysis suggests a structured means for defining latent-space projections that can permit provable performance properties to be established for machine learning methods.