SVD-AE: Simple Autoencoders for Collaborative Filtering

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 balanced CF in terms of the aforementioned tradeoffs.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.