In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing works. First, most works perform deep content feature learning and resort to matrix factorization, which cannot effectively model the highly complex user-item interaction function. Second, due to the difficulty on training deep neural networks, existing models utilize a shallow architecture, and thus limit the expressive potential of deep learning. Third, neural network models are easy to overfit on the implicit setting, because negative interactions are not taken into account. To tackle these issues, we present a generic recommender framework called Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit feedback. NCAE can effectively capture the relationship between interactions via a non-linear matrix factorization process. To optimize the deep architecture of NCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy. Extensive experiments on three real-world datasets demonstrate that NCAE can significantly advance the state-of-the-art.
翻译:近些年来,深层神经网络在几项任务上产生了最先进的表现。虽然最近的一些工程侧重于将深层次学习与建议相结合,但我们强调现有工程的三个问题。首先,大多数工程都具有深层内容特征学习和采用矩阵因子化,无法有效地模拟高度复杂的用户-项目互动功能。第二,由于培训深层神经网络的困难,现有模型使用浅质结构,从而限制了深层学习的深层潜力。第三,神经网络模型很容易过度适应隐含的设置,因为没有考虑到消极的相互作用。为了解决这些问题,我们提出了一个通用的建议框架,称为神经合作自动编码(NCAE),以进行协作过滤,这有利于明确的反馈和隐含的反馈。NCAE能够通过非线性矩阵因子化过程有效地捕捉到相互作用之间的关系。为了优化国家大气评估中心的深层结构,我们开发了一个三阶段的训练前机制,将监督和未经强化的特性学习结合起来。此外,为了防止在隐含的设置上出现过度,我们提议对三层的实验进行真正的错误程度的实验,并展示国家空间数据。