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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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（RSs）中最有效的算法之一。由于SVD算法具有迭代性质，因此面临的一大挑战是初始化，这对RS的收敛和性能产生了重大影响。不幸的是，文献中现有的SVD算法通常以随机的方式初始化用户和商品特征。因此，数据信息没有得到充分利用。这项工作解决了为SVD算法开发有效的初始化方法的挑战。我们提出了一个通用的神经嵌入初始化框架，其中一个低复杂度的概率自动编码器神经网络初始化了用户和商品的功能。该框架支持显式和隐式反馈数据集。我们提议的框架的设计细节已详细阐述和讨论。实验结果表明，基于我们提出的初始化框架的RS在评分预测方面优于最新方法。此外，关于项目排名，我们提出的框架显示出比现有的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％。