# AutoDenoise: Automatic Data Instance Denoising for Recommendations

arXiv: https://arxiv.org/abs/2303.06611 · HTML: https://mlsanity.com/paper/2303.06611/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2023.

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

- [Denoising Diffusion Recommender Model](https://mlsanity.com/paper/2401.06982/) (2024)
- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [Double Correction Framework for Denoising Recommendation](https://mlsanity.com/paper/2405.11272/) (2024)
- [Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM](https://mlsanity.com/paper/2312.15450/) (2023)
- [SIGMA: Selective Gated Mamba for Sequential Recommendation](https://mlsanity.com/paper/2408.11451/) (2024)
- [Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework](https://mlsanity.com/paper/2507.04636/) (2025)
- [BSL: Understanding and Improving Softmax Loss for Recommendation](https://mlsanity.com/paper/2312.12882/) (2023)
- [DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems](https://mlsanity.com/paper/2402.00390/) (2024)
- [Efficient and Robust Regularized Federated Recommendation](https://mlsanity.com/paper/2411.01540/) (2024)
- [SampleLLM: Optimizing Tabular Data Synthesis in Recommendations](https://mlsanity.com/paper/2501.16125/) (2025)
- [Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation](https://mlsanity.com/paper/2409.10343/) (2024)
- [GPRec: Bi-level User Modeling for Deep Recommenders](https://mlsanity.com/paper/2410.20730/) (2024)
- [Advancing Loss Functions in Recommender Systems: A Comparative Study with a R'enyi Divergence-Based Solution](https://mlsanity.com/paper/2506.15120/) (2025)
- [Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems](https://mlsanity.com/paper/2506.23090/) (2025)
- [Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs](https://mlsanity.com/paper/2509.02017/) (2025)
- [Measuring the Predictability of Recommender Systems using Structural Complexity Metrics](https://mlsanity.com/paper/2404.08829/) (2024)
- [When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation](https://mlsanity.com/paper/2409.12730/) (2024)
- [Enhancing Recommendation with Denoising Auxiliary Task](https://mlsanity.com/paper/2409.17402/) (2024)
- [From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals](https://mlsanity.com/paper/2508.10851/) (2025)
- [Empowering Denoising Sequential Recommendation with Large Language Model Embeddings](https://mlsanity.com/paper/2510.04239/) (2025)
- [Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models](https://mlsanity.com/paper/2511.07295/) (2025)
- [Renormalization Group Guided Tensor Network Structure Search](https://mlsanity.com/paper/2512.24663/) (2025)
- [Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks](https://mlsanity.com/paper/2605.06238/) (2026)

## Abstract (arXiv)

Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences. However, the arbitrary user behaviors in most recommendation scenarios lead to a large volume of noisy data instances being recorded, which cannot fully represent their true interests. While a large number of denoising studies are emerging in the recommender system community, all of them suffer from highly dynamic data distributions. In this paper, we propose a Deep Reinforcement Learning (DRL) based framework, AutoDenoise, with an Instance Denoising Policy Network, for denoising data instances with an instance selection manner in deep recommender systems. To be specific, AutoDenoise serves as an agent in DRL to adaptively select noise-free and predictive data instances, which can then be utilized directly in training representative recommendation models. In addition, we design an alternate two-phase optimization strategy to train and validate the AutoDenoise properly. In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability. We conduct extensive experiments to validate the effectiveness of AutoDenoise combined with multiple representative recommender system models.