6 citations · 16 across the 9 of their papers we have counts for
4 papers · 1 filter
An Admissible Shift-Consistent Method for Recommender Systems
Tung Nguyen, Jeffrey Uhlmann
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 guara…
Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems
Tung Nguyen, Jeffrey Uhlmann
In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e.g., AI-based. We demonstrate our approac…
A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System
Tung Nguyen, Sang T. Truong, Jeffrey Uhlmann
In this paper we provide a latent-variable formulation and solution to the recommender system (RS) problem in terms of a fundamental property that any reasonable solution should be…
Tensor Completion with Provable Consistency and Fairness Guarantees for Recommender Systems
Tung Nguyen, Jeffrey Uhlmann
We introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems. The novelty of the framework is that instead of a…