# Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems

arXiv: https://arxiv.org/abs/2201.03158 · HTML: https://mlsanity.com/paper/2201.03158/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 55 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 27 of 27)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems](https://mlsanity.com/paper/1905.04413/) (2019)
- [Session-based Social Recommendation via Dynamic Graph Attention Networks](https://mlsanity.com/paper/1902.09362/) (2019)
- [Fairness-Aware Explainable Recommendation over Knowledge Graphs](https://mlsanity.com/paper/2006.02046/) (2020)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [NAIS: Neural Attentive Item Similarity Model for Recommendation](https://mlsanity.com/paper/1809.07053/) (2018)
- [Knowledge-aware Coupled Graph Neural Network for Social Recommendation](https://mlsanity.com/paper/2110.03987/) (2021)
- [Outer Product-based Neural Collaborative Filtering](https://mlsanity.com/paper/1808.03912/) (2018)
- [Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.04000/) (2021)
- [Graph Meta Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.03969/) (2021)
- [Try This Instead: Personalized and Interpretable Substitute Recommendation](https://mlsanity.com/paper/2005.09344/) (2020)
- [Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network](https://mlsanity.com/paper/2110.04002/) (2021)
- [A Neural Autoregressive Approach to Collaborative Filtering](https://mlsanity.com/paper/1605.09477/) (2016)
- [Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation](https://mlsanity.com/paper/1904.12796/) (2019)
- [AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders](https://mlsanity.com/paper/1704.00551/) (2017)
- [Deep Item-based Collaborative Filtering for Top-N Recommendation](https://mlsanity.com/paper/1811.04392/) (2018)
- [Deep Adversarial Social Recommendation](https://mlsanity.com/paper/1905.13160/) (2019)
- [Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks](https://mlsanity.com/paper/1906.00150/) (2019)
- [Hybrid Recommender System based on Autoencoders](https://mlsanity.com/paper/1606.07659/) (2016)
- [Deep Landscape Forecasting for Real-time Bidding Advertising](https://mlsanity.com/paper/1905.03028/) (2019)
- [Joint Neural Collaborative Filtering for Recommender Systems](https://mlsanity.com/paper/1907.03459/) (2019)
- [Collaborative Filtering with User-Item Co-Autoregressive Models](https://mlsanity.com/paper/1612.07146/) (2016)

## Built on by (most cited first, 2 of 2)

- [S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain](https://mlsanity.com/paper/2501.00384/) (2024)
- [A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems](https://mlsanity.com/paper/2412.01378/) (2024)

## Abstract (arXiv)

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 https://github.com/akaxlh/CRANet.