# Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation

arXiv: https://arxiv.org/abs/2103.10693 · HTML: https://mlsanity.com/paper/2103.10693/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 19 Mar 2021.

## Builds on (most cited first, 20 of 20)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks](https://mlsanity.com/paper/1701.04722/) (2017)
- [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models](https://mlsanity.com/paper/1705.10513/) (2017)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [Adversarial Personalized Ranking for Recommendation](https://mlsanity.com/paper/1808.03908/) (2018)
- [Denoising Criterion for Variational Auto-Encoding Framework](https://mlsanity.com/paper/1511.06406/) (2015)
- [Variational Inference using Implicit Distributions](https://mlsanity.com/paper/1702.08235/) (2017)
- [Sequential Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1811.09975/) (2018)
- [Coupled Variational Recurrent Collaborative Filtering](https://mlsanity.com/paper/1906.04386/) (2019)

## Built on by (most cited first, 28 of 28)

- [D2C: Diffusion-Denoising Models for Few-shot Conditional Generation](https://mlsanity.com/paper/2106.06819/) (2021)
- [DiffuRec: A Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2304.00686/) (2023)
- [Conditional Denoising Diffusion for Sequential Recommendation](https://mlsanity.com/paper/2304.11433/) (2023)
- [DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models](https://mlsanity.com/paper/2408.12153/) (2024)
- [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)
- [Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model](https://mlsanity.com/paper/2410.23994/) (2024)
- [Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach](https://mlsanity.com/paper/2408.08931/) (2024)
- [Multi-Sample based Contrastive Loss for Top-k Recommendation](https://mlsanity.com/paper/2109.00217/) (2021)
- [Determinantal Point Process Likelihoods for Sequential Recommendation](https://mlsanity.com/paper/2204.11562/) (2022)
- [Sequential Recommendation with Diffusion Models](https://mlsanity.com/paper/2304.04541/) (2023)
- [Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/2312.10385/) (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)
- [Disentangled Representation for Diversified Recommendations](https://mlsanity.com/paper/2301.05492/) (2023)
- [FlowRec: Prior-Informed Flow Matching for Efficient Sequential Recommendation Generation](https://mlsanity.com/paper/2508.17618/) (2025)
- [C2VAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior](https://mlsanity.com/paper/2309.13303/) (2023)
- [DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model](https://mlsanity.com/paper/2410.02791/) (2024)
- [Generative Diffusion Models for Sequential Recommendations](https://mlsanity.com/paper/2410.19429/) (2024)
- [Listwise Preference Alignment Optimization for Tail Item Recommendation](https://mlsanity.com/paper/2507.02255/) (2025)
- [Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model](https://mlsanity.com/paper/2506.19777/) (2025)
- [Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up](https://mlsanity.com/paper/2302.14395/) (2023)
- [G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://mlsanity.com/paper/2306.14314/) (2023)
- [Heterogeneous Sequel-Aware Graph Neural Networks for Sequential Learning](https://mlsanity.com/paper/2506.05625/) (2025)
- [Asymmetric Diffusion Recommendation Model](https://mlsanity.com/paper/2508.12706/) (2025)
- [Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring](https://mlsanity.com/paper/2603.04986/) (2026)
- [MGDiff: Multi-Interest Sequence Recommendation with Masking GNN-Guided Diffusion](https://mlsanity.com/paper/2609.01619/) (2026)
- [FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models](https://mlsanity.com/paper/2609.36671/) (2026)

## Abstract (arXiv)

Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism have achieved good performance in sequential recommendation. Recently, the generative models based on Variational Autoencoder (VAE) have shown the unique advantage in collaborative filtering. In particular, the sequential VAE model as a recurrent version of VAE can effectively capture temporal dependencies among items in user sequence and perform sequential recommendation. However, VAE-based models suffer from a common limitation that the representational ability of the obtained approximate posterior distribution is limited, resulting in lower quality of generated samples. This is especially true for generating sequences. To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive Variational Autoencoder (ACVAE) for sequential recommendation. Specifically, we first introduce the adversarial training for sequence generation under the Adversarial Variational Bayes (AVB) framework, which enables our model to generate high-quality latent variables. Then, we employ the contrastive loss. The latent variables will be able to learn more personalized and salient characteristics by minimizing the contrastive loss. Besides, when encoding the sequence, we apply a recurrent and convolutional structure to capture global and local relationships in the sequence. Finally, we conduct extensive experiments on four real-world datasets. The experimental results show that our proposed ACVAE model outperforms other state-of-the-art methods.