# Efficient Bi-Level Optimization for Recommendation Denoising

arXiv: https://arxiv.org/abs/2210.10321 · HTML: https://mlsanity.com/paper/2210.10321/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Adversarial Attacks on Graph Neural Networks via Meta Learning](https://mlsanity.com/paper/1902.08412/) (2019)
- [Dataset Distillation with Infinitely Wide Convolutional Networks](https://mlsanity.com/paper/2107.13034/) (2021)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [Graph Condensation for Graph Neural Networks](https://mlsanity.com/paper/2110.07580/) (2021)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://mlsanity.com/paper/2101.06448/) (2021)
- [Condensing Graphs via One-Step Gradient Matching](https://mlsanity.com/paper/2206.07746/) (2022)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [Denoising Implicit Feedback for Recommendation](https://mlsanity.com/paper/2006.04153/) (2020)
- [AutoDebias: Learning to Debias for Recommendation](https://mlsanity.com/paper/2105.04170/) (2021)
- [Towards Representation Alignment and Uniformity in Collaborative Filtering](https://mlsanity.com/paper/2206.12811/) (2022)
- [Adversarial Personalized Ranking for Recommendation](https://mlsanity.com/paper/1808.03908/) (2018)
- [Socially-Aware Self-Supervised Tri-Training for Recommendation](https://mlsanity.com/paper/2106.03569/) (2021)
- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Self-Guided Learning to Denoise for Robust Recommendation](https://mlsanity.com/paper/2204.06832/) (2022)
- [Unlearning Protected User Attributes in Recommendations with Adversarial Training](https://mlsanity.com/paper/2206.04500/) (2022)
- [Learning Robust Recommenders through Cross-Model Agreement](https://mlsanity.com/paper/2105.09605/) (2021)
- [Sampler Design for Bayesian Personalized Ranking by Leveraging View Data](https://mlsanity.com/paper/1809.08162/) (2018)
- [Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack](https://mlsanity.com/paper/2107.10457/) (2021)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)

## Built on by (most cited first, 20 of 20)

- [Denoising Diffusion Recommender Model](https://mlsanity.com/paper/2401.06982/) (2024)
- [Double Correction Framework for Denoising Recommendation](https://mlsanity.com/paper/2405.11272/) (2024)
- [Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks](https://mlsanity.com/paper/2311.18244/) (2023)
- [LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation](https://mlsanity.com/paper/2408.08208/) (2024)
- [ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems](https://mlsanity.com/paper/2409.11690/) (2024)
- [Symmetric Graph Contrastive Learning against Noisy Views for Recommendation](https://mlsanity.com/paper/2408.02691/) (2024)
- [Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation](https://mlsanity.com/paper/2409.10343/) (2024)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection](https://mlsanity.com/paper/2508.12632/) (2025)
- [Advancing Loss Functions in Recommender Systems: A Comparative Study with a R'enyi Divergence-Based Solution](https://mlsanity.com/paper/2506.15120/) (2025)
- [PAMAS: Self-Adaptive Multi-Agent System with Perspective Aggregation for Misinformation Detection](https://mlsanity.com/paper/2602.03158/) (2026)
- [Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems](https://mlsanity.com/paper/2604.10029/) (2026)
- [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)
- [DeBaTeR: Denoising Bipartite Temporal Graph for Recommendation](https://mlsanity.com/paper/2411.09181/) (2024)
- [Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2501.13344/) (2025)
- [From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals](https://mlsanity.com/paper/2508.10851/) (2025)
- [Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models](https://mlsanity.com/paper/2511.07295/) (2025)
- [Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrix](https://mlsanity.com/paper/2605.20721/) (2026)
- [Denoising Implicit Feedback for Cold-start Recommendation](https://mlsanity.com/paper/2606.19658/) (2026)

## Abstract (arXiv)

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.