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 this https URL.
Abstract (translated)
协作过滤（CF）方法在推荐系统领域得到了广泛研究，从矩阵分解和自编码器为基础到图过滤为基础的方法。最近，人们提出了一些轻量级的方法，几乎不需要训练，以降低总计算量。然而，现有的方法在准确性和效率之间仍存在潜在的权衡。特别是，在上述权衡方面，没有得到良好设计的闭合形式研究。在本文中，我们设计了一个简单的 yet 有效的 singular vector decomposition (SVD)-based linear autoencoder，称为 SVD-AE，其闭合形式解决方案可以根据 SVD 定义。SVD-AE 不需要迭代训练过程，因为其闭合形式解决方案可以一次性计算出来。此外，考虑到评分矩阵的噪声性质，我们研究了现有 CF 方法的鲁棒性，以及我们 SVD-AE 对这种噪声交互的鲁棒性。结果，我们证明了基于截断 SVD 的简单设计选择可以用于增强推荐系统的噪声鲁棒性，同时提高效率。代码可在此处访问：https://www.acm.org/dl/doi/10.1145/2848006.2848015
URL
https://arxiv.org/abs/2405.04746
