SVD-AE: Simple Autoencoders for Collaborative Filtering
This work addresses the need for balanced collaborative filtering methods in recommendation systems, though it appears incremental as it builds on existing SVD and autoencoder approaches.
The paper tackles the problem of improving trade-offs among accuracy, efficiency, and robustness in collaborative filtering for recommendation systems by proposing SVD-AE, a simple SVD-based linear autoencoder with a closed-form solution, which enhances noise robustness and efficiency without iterative training.
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.