As the deep learning techniques have expanded to real-world recommendation
tasks, many deep neural network based Collaborative Filtering (CF) models have
been developed to project user-item interactions into latent feature space,
based on various neural architectures, such as multi-layer perceptron,
auto-encoder and graph neural networks. However, the majority of existing
collaborative filtering systems are not well designed to handle missing data.
Particularly, in order to inject the negative signals in the training phase,
these solutions largely rely on negative sampling from unobserved user-item
interactions and simply treating them as negative instances, which brings the
recommendation performance degradation. To address the issues, we develop a
Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is
capable of exploring transferable knowledge from observed and unobserved
user-item interactions. The network architecture of CRANet is formed of an
integrative structure with a reflective receptor network and an information
fusion autoencoder module, which endows our recommendation framework with the
ability of encoding implicit user's pairwise preference on both interacted and
non-interacted items. Additionally, a parametric regularization-based
tied-weight scheme is designed to perform robust joint training of the
two-stage CRANet model. We finally experimentally validate CRANet on four
diverse benchmark datasets corresponding to two recommendation tasks, to show
that debiasing the negative signals of user-item interactions improves the
performance as compared to various state-of-the-art recommendation techniques.
Our source code is available at this https URL.

文章提出了 Collaborative Reflection-Augmented Autoencoder Network (CRANet) 相关理论，旨在解决在训练阶段中由缺省数据引入的负信号所带来的推荐性能下降问题，并通过实验验证 CRANet 相比于其他推荐技术可以显著改善推荐问题的性能。