Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback

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 expressiveness 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 novel recommender framework called Deep Collaborative Autoencoder (DCAE) for both explicit feedback and implicit feedback, which can effectively capture the relationship between interactions via its non-linear expressiveness. To optimize the deep architecture of DCAE, we develop a three-stage pretraining mechanism that combines supervised and unsupervised feature learning. Moreover, we propose a popularity-based error reweighting module and a sparsity-aware data-augmentation strategy for DCAE to prevent overfitting on the implicit setting. Extensive experiments on three real-world datasets demonstrate that DCAE can significantly advance the state-of-the-art.