Recently, various deep learning-based models have been applied in the study of recommender systems. Some researches have combined the classic collaborative filtering method with deep learning frameworks in order to obtain more accurate recommendations. However, these models either add additional features, but still recommend in the original linear manner, or only extract the global latent factors of the rating matrices in a non-linear way without considering some local special relationships. In this paper, we propose a deep learning framework for explicit recommender systems, named Attention Collaborative Autoencoder (ACAE). Based on the denoising autoencoder, our model can extract the global latent factors in a non-linear fashion from the sparse rating matrices. In ACAE, attention units are introduced during back propagation, enabling discovering potential relationships between users and items in the neighborhood, which makes the model obtain better results in the rating prediction tasks. In addition, we propose how to optimize the training process of the model by proposing a new loss function. Experiments on two public datasets demonstrate the effectiveness of ACAE and its outperformance of competitive baselines.

中文翻译：

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显式推荐系统的Attention协作自动编码器

最近，各种基于深度学习的模型已应用于推荐系统的研究中。一些研究将经典的协作过滤方法与深度学习框架结合在一起，以获得更准确的建议。但是，这些模型要么添加了其他功能，但仍建议以原始线性方式使用，或者仅以非线性方式提取评分矩阵的全局潜在因子，而无需考虑某些局部特殊关系。在本文中，我们提出了一个针对显式推荐系统的深度学习框架，称为“注意力协作自动编码器（ACAE）”。基于去噪自动编码器，我们的模型可以从稀疏评级矩阵中以非线性方式提取全局潜在因子。在ACAE中，注意单元是在反向传播过程中引入的，支持发现用户与附近项目之间的潜在关系，从而使模型在评级预测任务中获得更好的结果。另外，我们提出了如何通过提出新的损失函数来优化模型的训练过程。在两个公共数据集上的实验证明了ACAE的有效性及其在竞争基准方面的出色表现。