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A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems

arXiv 2412.01378 ↗First posted 2 Dec 2024cs.AI cs.LGcited by 3

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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.

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

23 of 54 references.

References · 23

  1. 2017Neural Collaborative Filteringcited by 1,111
  2. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  3. 2020Neural Collaborative Filtering vs. Matrix Factorization Revisitedcited by 123
  4. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 120
  5. 2020Disentangled Graph Collaborative Filteringcited by 118
  6. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  7. 2019RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedbackcited by 63
  8. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  9. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  10. 2021Dynamic Graph Collaborative Filteringcited by 33
  11. 2018Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviorscited by 27
  12. 2023Disentangled Contrastive Collaborative Filteringcited by 22
11 more
  1. 2022MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filteringcited by 11
  2. 2023Graph-less Collaborative Filteringcited by 11
  3. 2021Neural Graph Matching based Collaborative Filteringcited by 9
  4. 2021Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filteringcited by 8
  5. 2021Localized Graph Collaborative Filteringcited by 6
  6. 2022GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendationcited by 6
  7. 2022Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspectivecited by 4
  8. 2018Wasserstein Autoencoders for Collaborative Filteringcited by 2
  9. 2020COMET: Convolutional Dimension Interaction for Collaborative Filteringcited by 2
  10. 2022Collaborative Reflection-Augmented Autoencoder Network for Recommender Systemscited by 2
  11. 2020Deep Neural Review Text Interaction for Recommendation Systemscited by 1

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025A Survey on Large Language Models in Multimodal Recommender Systemscited by 7
  2. 2025From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendationcited by 2
  3. 2025Retentive Relevance: Capturing Long-Term User Value in Recommendation Systemscited by 1