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

• URL: http://arxiv.org/abs/2206.13355v1
• Date: Mon, 27 Jun 2022 15:03:18 GMT
• Title: A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System
• Authors: Tung Nguyen, Sang T. Truong, and Jeffrey Uhlmann
• Abstract summary: We study a novel 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.
• Score: 6.125831939376033
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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