The sparsity problem remains a significant bottleneck for recommendation systems. In recent years, deep matrix factorization has shown promising results in mitigating this issue. Furthermore, many works have improved the prediction accuracy of deep matrix factorization by incorporating the user’s and/or items’ auxiliary information. However, there are still two remaining drawbacks that need to be addressed. First, the initialization of latent feature representations has a substantial impact on the performance of deep matrix factorization, and most current models utilize a uniform approach to this initialization, constraining the model’s optimization potential. Secondly, many existing recommendation models lack versatility and efficiency in transferring auxiliary information from users or items to expand the feature space. This paper proposes a novel model to address the issues mentioned above. By using a semi-autoencoder, the pre-trained initialization of the latent feature representation is realized in this paper. Simultaneously, this model assimilates auxiliary information, like item attributes or rating matrices from diverse domains, to generate their latent feature representations. These representations are then transferred to the target task through subspace projection distance. With this, this model can utilize auxiliary information from various sources more efficiently and this model has better versatility. This is called deep matrix factorization via feature subspace transfer. Numerical experiments on several real-world data show the improvement of this method compared with state-of-the-art methods of introducing auxiliary information about items. Compared with the deep matrix factorization model, the proposed model can achieve 6.5% improvement at most in the mean absolute error and root mean square error.

稀疏性问题仍然是推荐系统的一个重要瓶颈。近年来，深度矩阵分解在缓解这一问题方面显示出了可喜的成果。此外，许多工作通过合并用户和/或项目的辅助信息提高了深度矩阵分解的预测精度。然而，仍有两个缺陷需要解决。首先，潜在特征表示的初始化对深度矩阵分解的性能有很大影响，并且大多数当前模型都采用统一的方法来进行初始化，从而限制了模型的优化潜力。其次，许多现有的推荐模型在传递来自用户或项目的辅助信息以扩展特征空间方面缺乏通用性和效率。本文提出了一种新的模型来解决上述问题。本文通过使用半自动编码器，实现了潜在特征表示的预训练初始化。同时，该模型吸收辅助信息，例如来自不同领域的项目属性或评级矩阵，以生成其潜在特征表示。然后，这些表示通过子空间投影距离转移到目标任务。这样，该模型可以更有效地利用各种来源的辅助信息，并且该模型具有更好的通用性。这称为通过特征子空间转移进行深度矩阵分解。对几个现实世界数据的数值实验表明，与引入项目辅助信息的最先进方法相比，该方法有所改进。与深度矩阵分解模型相比，该模型在平均绝对误差和均方根误差上最多可以实现6.5%的改进。