SVD-AE: Simple Autoencoders for Collaborative Filtering

TL;DR

This paper introduces SVD-AE, a simple linear autoencoder for collaborative filtering that leverages a closed-form SVD solution, enhancing efficiency and robustness without iterative training.

Contribution

The paper presents a novel SVD-based autoencoder with a closed-form solution for collaborative filtering, eliminating the need for iterative training and improving noise robustness.

Findings

SVD-AE achieves comparable accuracy to more complex models.

It significantly reduces training time due to its closed-form solution.

SVD-AE demonstrates improved robustness against noisy rating data.

Abstract

Collaborative filtering (CF) methods for recommendation systems have been extensively researched, ranging from matrix factorization and autoencoder-based to graph filtering-based methods. Recently, lightweight methods that require almost no training have been recently proposed to reduce overall computation. However, existing methods still have room to improve the trade-offs among accuracy, efficiency, and robustness. In particular, there are no well-designed closed-form studies for \emph{balanced} CF in terms of the aforementioned trade-offs. In this paper, we design SVD-AE, a simple yet effective singular vector decomposition (SVD)-based linear autoencoder, whose closed-form solution can be defined based on SVD for CF. SVD-AE does not require iterative training processes as its closed-form solution can be calculated at once. Furthermore, given the noisy nature of the rating matrix, we explore the robustness against such noisy interactions of existing CF methods and our SVD-AE. As a result, we demonstrate that our simple design choice based on truncated SVD can be used to strengthen the noise robustness of the recommendation while improving efficiency. Code is available at https://github.com/seoyoungh/svd-ae.