Singular value decomposition (SVD) is one of the most effective algorithms in recommender systems (RSs). Due to the iterative nature of SVD algorithms, one big challenge is initialization that has a major impact on the convergence and performance of RSs. Unfortunately, existing SVD algorithms in the literature typically initialize the user and item features in a random manner; thus, data information is not fully utilized. This work addresses the challenge of developing an efficient initialization method for SVD algorithms. We propose a general neural embedding initialization framework, where a low-complexity probabilistic autoencoder neural network initializes the features of user and item. This framework supports explicit and implicit feedback data sets. The design details of our proposed framework are elaborated and discussed. Experimental results show that RSs based on our proposed initialization framework outperform the state-of-the-art methods in rating prediction. Moreover, regarding item ranking, our proposed framework shows an improvement of at least 2.20% ~5.74% than existing SVD algorithms and other matrix factorization methods in the literature.

中文翻译：

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用于协同过滤的神经嵌入奇异值分解


奇异值分解（SVD）是推荐系统（RS）中最有效的算法之一。由于 SVD 算法的迭代性质，初始化是一大挑战，它对 RS 的收敛和性能有重大影响。不幸的是，文献中现有的 SVD 算法通常以随机方式初始化用户和项目特征；因此，数据信息没有得到充分利用。这项工作解决了为 SVD 算法开发有效初始化方法的挑战。我们提出了一种通用的神经嵌入初始化框架，其中低复杂度的概率自动编码器神经网络初始化用户和项目的特征。该框架支持显式和隐式反馈数据集。详细阐述和讨论了我们提出的框架的设计细节。实验结果表明，基于我们提出的初始化框架的 RS 在评级预测方面优于最先进的方法。此外，在项目排序方面，我们提出的框架比现有的 SVD 算法和文献中的其他矩阵分解方法至少提高了 2.20% ~5.74%。