A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System

TL;DR

This paper introduces a scalable tensor completion algorithm for recommendation systems that leverages a latent invariant constraint, ensuring unique, consistent, and preference-independent predictions with linear complexity.

Contribution

It proposes a novel tensor completion method regularized by a latent invariant, achieving uniqueness, unit consistency, and ranking guarantees without hyperparameter tuning.

Findings

Outperforms state-of-the-art methods in accuracy

Ensures unique tensor completion results

Provides consistent ranking between observed and unobserved ratings

Abstract

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.