mlsanityData to 14 Oct 2026

Variational Autoencoders for Collaborative Filtering

arXiv 1802.05814 ↗First posted 16 Feb 2018stat.ML cs.IR cs.LGcited by 310

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Cited by 32 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. Despite widespread use in language modeling and economics, the multinomial likelihood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm has information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. Empirically, we show that the proposed approach significantly outperforms several state-of-the-art baselines, including two recently-proposed neural network approaches, on several real-world datasets. We also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. Finally, we identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements.

Key facts. Cited by 310 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Feb 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 49; 2022: 61; 2023: 59; 2024: 67; 2025: 48; 2026: 26.

310 papers.

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  1. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 127
  2. 2023DiffuRec: A Diffusion Model for Sequential Recommendationcited by 93
  3. 2023On Generative Agents in Recommendationcited by 90
  4. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  5. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  6. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  7. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  8. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  9. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  10. 2021Identifiable Deep Generative Models via Sparse Decodingcited by 54
  11. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  12. 2021Consistency Regularization for Variational Auto-Encoderscited by 51
38 more
  1. 2021How Powerful is Graph Convolution for Recommendation?cited by 50
  2. 2023White-box Membership Inference Attacks against Diffusion Modelscited by 42
  3. 2023DiffKG: Knowledge Graph Diffusion Model for Recommendationcited by 41
  4. 2022Blurring-Sharpening Process Models for Collaborative Filteringcited by 35
  5. 2022HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularizationcited by 34
  6. 2025SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluationcited by 33
  7. 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
  8. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  9. 2024Diffusion Models for Generative Outfit Recommendationcited by 29
  10. 2021Adversarial and Contrastive Variational Autoencoder for Sequential Recommendationcited by 28
  11. 2024Denoising Diffusion Recommender Modelcited by 28
  12. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 28
  13. 2021A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Modelscited by 25
  14. 2023Graph Signal Diffusion Model for Collaborative Filteringcited by 25
  15. 2022HICF: Hyperbolic Informative Collaborative Filteringcited by 24
  16. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  17. 2023Conditional Denoising Diffusion for Sequential Recommendationcited by 23
  18. 2021Bootstrapping User and Item Representations for One-Class Collaborative Filteringcited by 22
  19. 2022A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendationcited by 22
  20. 2023Knowledge Graph Self-Supervised Rationalization for Recommendationcited by 22
  21. 2024DiffMM: Multi-Modal Diffusion Model for Recommendationcited by 22
  22. 2024DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Modelscited by 22
  23. 2021LT-OCF: Learnable-Time ODE-based Collaborative Filteringcited by 21
  24. 2021Revisiting the Performance of iALS on Item Recommendation Benchmarkscited by 21
  25. 2022Infinite Recommendation Networks: A Data-Centric Approachcited by 20
  26. 2022Unlearning Protected User Attributes in Recommendations with Adversarial Trainingcited by 20
  27. 2024RecDiff: Diffusion Model for Social Recommendationcited by 20
  28. 2022DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluationcited by 19
  29. 2022ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendationcited by 19
  30. 2023Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendationcited by 19
  31. 2024ContextGNN: Beyond Two-Tower Recommendation Systemscited by 19
  32. 2021Towards a Better Understanding of Linear Models for Recommendationcited by 18
  33. 2024Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Modelscited by 18
  34. 2024Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivitycited by 18
  35. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  36. 2022Amortized Variational Inference: A Systematic Reviewcited by 16
  37. 2024Dual Test-time Training for Out-of-distribution Recommender Systemcited by 16
  38. 2021Learning Robust Recommenders through Cross-Model Agreementcited by 15

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