# Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training

arXiv: https://arxiv.org/abs/2304.05492 · HTML: https://mlsanity.com/paper/2304.05492/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [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)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Adversarial Training Can Hurt Generalization](https://mlsanity.com/paper/1906.06032/) (2019)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](https://mlsanity.com/paper/2108.06625/) (2021)
- [Adversarial Personalized Ranking for Recommendation](https://mlsanity.com/paper/1808.03908/) (2018)
- [Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer](https://mlsanity.com/paper/2105.00522/) (2021)
- [CosRec: 2D Convolutional Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/1908.09972/) (2019)
- [Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction](https://mlsanity.com/paper/2109.01165/) (2021)
- [Adversarial Examples in Modern Machine Learning: A Review](https://mlsanity.com/paper/1911.05268/) (2019)
- [Practical Data Poisoning Attack against Next-Item Recommendation](https://mlsanity.com/paper/2004.03728/) (2020)
- [Adversarial Sampling and Training for Semi-Supervised Information Retrieval](https://mlsanity.com/paper/1811.04155/) (2018)
- [Rank List Sensitivity of Recommender Systems to Interaction Perturbations](https://mlsanity.com/paper/2201.12686/) (2022)
- [RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems](https://mlsanity.com/paper/2201.04399/) (2022)

## Built on by (most cited first, 2 of 2)

- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation](https://mlsanity.com/paper/2410.10835/) (2024)

## Abstract (arXiv)

Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of sequential recommendation models, their robustness has recently come into question. Two properties unique to the nature of sequential recommendation models may impair their robustness - the cascade effects induced during training and the model's tendency to rely too heavily on temporal information. To address these vulnerabilities, we propose Cascade-guided Adversarial training, a new adversarial training procedure that is specifically designed for sequential recommendation models. Our approach harnesses the intrinsic cascade effects present in sequential modeling to produce strategic adversarial perturbations to item embeddings during training. Experiments on training state-of-the-art sequential models on four public datasets from different domains show that our training approach produces superior model ranking accuracy and superior model robustness to real item replacement perturbations when compared to both standard model training and generic adversarial training.