A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System
This addresses the need for reliable and efficient recommendation systems, offering a novel approach with strong empirical gains.
The paper tackles the recommender system problem by introducing a tensor completion method with a latent invariant constraint, achieving uniqueness, unit consistency, and consensus ordering, and it significantly outperforms state-of-the-art methods with linear computational complexity and no hyperparameter tuning.
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 expected to satisfy. Specifically, we examine a novel tensor completion method to efficiently and accurately learn parameters of a model for the unobservable personal preferences that underly user ratings. By regularizing the tensor decomposition with a single latent invariant, we achieve three properties for a reliable recommender system: (1) uniqueness of the tensor completion result with minimal assumptions, (2) unit consistency that is independent of arbitrary preferences of users, and (3) a consensus ordering guarantee that provides consistent ranking between observed and unobserved rating scores. Our algorithm leads to a simple and elegant recommendation framework that has linear computational complexity and with no hyperparameter tuning. We provide empirical results demonstrating that the approach significantly outperforms current state-of-the-art methods.