# Sequential Variational Autoencoders for Collaborative Filtering

arXiv: https://arxiv.org/abs/1811.09975 · HTML: https://mlsanity.com/paper/1811.09975/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 30 of 31)

- [DiffuRec: A Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2304.00686/) (2023)
- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation](https://mlsanity.com/paper/2103.10693/) (2021)
- [Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency](https://mlsanity.com/paper/2401.10545/) (2024)
- [Conditional Denoising Diffusion for Sequential Recommendation](https://mlsanity.com/paper/2304.11433/) (2023)
- [Infinite Recommendation Networks: A Data-Centric Approach](https://mlsanity.com/paper/2206.02626/) (2022)
- [ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation](https://mlsanity.com/paper/2209.00456/) (2022)
- [Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation](https://mlsanity.com/paper/2501.17670/) (2025)
- [Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering](https://mlsanity.com/paper/2109.05773/) (2021)
- [On Sampling Collaborative Filtering Datasets](https://mlsanity.com/paper/2201.04768/) (2022)
- [Sequential Recommendation with Diffusion Models](https://mlsanity.com/paper/2304.04541/) (2023)
- [Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems](https://mlsanity.com/paper/2304.09097/) (2023)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Bridging User Dynamics: Transforming Sequential Recommendations with Schr"odinger Bridge and Diffusion Models](https://mlsanity.com/paper/2409.10522/) (2024)
- [Trajectory Anomaly Detection with Language Models](https://mlsanity.com/paper/2409.15366/) (2024)
- [Correcting the User Feedback-Loop Bias for Recommendation Systems](https://mlsanity.com/paper/2109.06037/) (2021)
- [Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems](https://mlsanity.com/paper/2311.03488/) (2023)
- [SVD-AE: Simple Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/2405.04746/) (2024)
- [Addressing Missing Data Issue for Diffusion-based Recommendation](https://mlsanity.com/paper/2505.12283/) (2025)
- [Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach](https://mlsanity.com/paper/2505.19544/) (2025)
- [Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation](https://mlsanity.com/paper/2111.10539/) (2021)
- [Farzi Data: Autoregressive Data Distillation](https://mlsanity.com/paper/2310.09983/) (2023)
- [Generalized User Representations for Transfer Learning](https://mlsanity.com/paper/2403.00584/) (2024)
- [Generative Diffusion Models for Sequential Recommendations](https://mlsanity.com/paper/2410.19429/) (2024)
- [Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders](https://mlsanity.com/paper/2308.06885/) (2023)
- [Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity](https://mlsanity.com/paper/2410.12086/) (2024)
- [Brownian Bridge Diffusion for Sequential Recommendation](https://mlsanity.com/paper/2507.06121/) (2025)
- [Graph Enhanced Trajectory Anomaly Detection](https://mlsanity.com/paper/2509.18386/) (2025)
- [CVTT: Cross-Validation Through Time](https://mlsanity.com/paper/2205.05393/) (2022)

## Abstract (arXiv)

Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. In this work, we propose a model which extends variational autoencoders by exploiting the rich information present in the past preference history. We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network. At each time-step of the RNN, the sequence is fed through a series of fully-connected layers, the output of which models the probability distribution of the most likely future preferences. We show that handling temporal information is crucial for improving the accuracy of the VAE: In fact, our model beats the current state-of-the-art by valuable margins because of its ability to capture temporal dependencies among the user-consumption sequence using the recurrent encoder still keeping the fundamentals of variational autoencoders intact.