# A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems

arXiv: https://arxiv.org/abs/2412.01378 · HTML: https://mlsanity.com/paper/2412.01378/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 54 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 23 of 23)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach](https://mlsanity.com/paper/2001.10167/) (2020)
- [Disentangled Graph Collaborative Filtering](https://mlsanity.com/paper/2007.01764/) (2020)
- [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://mlsanity.com/paper/2202.06200/) (2022)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [Hypergraph Contrastive Collaborative Filtering](https://mlsanity.com/paper/2204.12200/) (2022)
- [Graph Trend Filtering Networks for Recommendations](https://mlsanity.com/paper/2108.05552/) (2021)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors](https://mlsanity.com/paper/1808.01075/) (2018)
- [Disentangled Contrastive Collaborative Filtering](https://mlsanity.com/paper/2305.02759/) (2023)
- [MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering](https://mlsanity.com/paper/2204.02338/) (2022)
- [Graph-less Collaborative Filtering](https://mlsanity.com/paper/2303.08537/) (2023)
- [Neural Graph Matching based Collaborative Filtering](https://mlsanity.com/paper/2105.04067/) (2021)
- [Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering](https://mlsanity.com/paper/2109.05773/) (2021)
- [Localized Graph Collaborative Filtering](https://mlsanity.com/paper/2108.04475/) (2021)
- [GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation](https://mlsanity.com/paper/2205.09948/) (2022)
- [Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective](https://mlsanity.com/paper/2208.09979/) (2022)
- [Wasserstein Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1809.05662/) (2018)
- [COMET: Convolutional Dimension Interaction for Collaborative Filtering](https://mlsanity.com/paper/2007.14129/) (2020)
- [Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems](https://mlsanity.com/paper/2201.03158/) (2022)
- [Deep Neural Review Text Interaction for Recommendation Systems](https://mlsanity.com/paper/2003.07051/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Survey on Large Language Models in Multimodal Recommender Systems](https://mlsanity.com/paper/2505.09777/) (2025)
- [From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation](https://mlsanity.com/paper/2509.23649/) (2025)
- [Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems](https://mlsanity.com/paper/2510.07621/) (2025)

## Abstract (arXiv)

This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF techniques face limitations in scalability and flexibility. DNNs can address these challenges by effectively modeling complex, non-linear relationships within the data. We begin by exploring the fundamental principles of both collaborative filtering and deep neural networks, laying the groundwork for understanding their integration. Subsequently, we review key advancements in the field, categorizing various deep learning models that enhance CF systems, including Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), autoencoders, Generative Adversarial Networks (GAN), and Restricted Boltzmann Machines (RBM). The paper also discusses evaluation protocols, various publicly available auxiliary information, and data features. Furthermore, the survey concludes with a discussion of the challenges and future research opportunities in enhancing collaborative filtering systems with deep learning.