# Adversarial Sampling and Training for Semi-Supervised Information Retrieval

arXiv: https://arxiv.org/abs/1811.04155 · HTML: https://mlsanity.com/paper/1811.04155/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 23 of 23)

- [ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data](https://mlsanity.com/paper/2107.04954/) (2021)
- [On the Theories Behind Hard Negative Sampling for Recommendation](https://mlsanity.com/paper/2302.03472/) (2023)
- [Does Negative Sampling Matter? A Review with Insights into its Theory and Applications](https://mlsanity.com/paper/2402.17238/) (2024)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling](https://mlsanity.com/paper/2206.11549/) (2022)
- [Unsupervised Corpus Poisoning Attacks in Continuous Space for Dense Retrieval](https://mlsanity.com/paper/2504.17884/) (2025)
- [Reproducing HotFlip for Corpus Poisoning Attacks in Dense Retrieval](https://mlsanity.com/paper/2501.04802/) (2025)
- [Learning Explicit User Interest Boundary for Recommendation](https://mlsanity.com/paper/2111.11026/) (2021)
- [Learning To Rank Resources with GNN](https://mlsanity.com/paper/2304.07946/) (2023)
- [From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning](https://mlsanity.com/paper/2406.15044/) (2024)
- [Generating Negative Samples for Sequential Recommendation](https://mlsanity.com/paper/2208.03645/) (2022)
- [Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training](https://mlsanity.com/paper/2304.05492/) (2023)
- [Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering](https://mlsanity.com/paper/2306.15905/) (2023)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning](https://mlsanity.com/paper/2607.26339/) (2026)
- [Bayesian Negative Sampling for Recommendation](https://mlsanity.com/paper/2204.06520/) (2022)
- [Towards Robust Recommender Systems via Triple Cooperative Defense](https://mlsanity.com/paper/2210.13762/) (2022)
- [Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality](https://mlsanity.com/paper/2107.13876/) (2021)
- [Knowledge Graph-enhanced Sampling for Conversational Recommender System](https://mlsanity.com/paper/2110.06637/) (2021)
- [Securing Recommender System via Cooperative Training](https://mlsanity.com/paper/2401.12700/) (2024)
- [MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation](https://mlsanity.com/paper/2502.08161/) (2025)
- [From Noise to Order: Learning to Rank via Denoising Diffusion](https://mlsanity.com/paper/2602.11453/) (2026)
- [Denoising Implicit Feedback for Cold-start Recommendation](https://mlsanity.com/paper/2606.19658/) (2026)

## Abstract (arXiv)

Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (ii) to obtain very informational non-clicked examples by adversarial sampling and training. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. Experimental results indicate that the proposed approaches significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task.