# From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals

arXiv: https://arxiv.org/abs/2508.10851 · HTML: https://mlsanity.com/paper/2508.10851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 14 Aug 2025.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Denoising Implicit Feedback for Recommendation](https://mlsanity.com/paper/2006.04153/) (2020)
- [Denoising Diffusion Recommender Model](https://mlsanity.com/paper/2401.06982/) (2024)
- [Self-Guided Learning to Denoise for Robust Recommendation](https://mlsanity.com/paper/2204.06832/) (2022)
- [AutoDenoise: Automatic Data Instance Denoising for Recommendations](https://mlsanity.com/paper/2303.06611/) (2023)
- [Efficient Bi-Level Optimization for Recommendation Denoising](https://mlsanity.com/paper/2210.10321/) (2022)
- [Double Correction Framework for Denoising Recommendation](https://mlsanity.com/paper/2405.11272/) (2024)
- [Learning Robust Recommenders through Cross-Model Agreement](https://mlsanity.com/paper/2105.09605/) (2021)
- [SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models](https://mlsanity.com/paper/2503.13503/) (2025)
- [Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction](https://mlsanity.com/paper/2012.06968/) (2020)
- [Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback](https://mlsanity.com/paper/2406.12501/) (2024)
- [Knowledge-refined Denoising Network for Robust Recommendation](https://mlsanity.com/paper/2304.14987/) (2023)
- [Unleashing the Power of Large Language Model for Denoising Recommendation](https://mlsanity.com/paper/2502.09058/) (2025)
- [Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning](https://mlsanity.com/paper/2406.13235/) (2024)
- [Personalized Denoising Implicit Feedback for Robust Recommender System](https://mlsanity.com/paper/2502.00348/) (2025)
- [Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback](https://mlsanity.com/paper/2204.04397/) (2022)
- [TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems](https://mlsanity.com/paper/2507.03895/) (2025)

## Abstract (arXiv)

Implicit feedback is central to modern recommender systems but is inherently noisy, often impairing model training and degrading user experience. At scale, such noise can mislead learning processes, reducing both recommendation accuracy and platform value. Existing denoising strategies typically overlook the entity-specific nature of noise while introducing high computational costs and complex hyperparameter tuning. To address these challenges, we propose EARD (Entity-Aware Reliability-Driven Denoising), a lightweight framework that shifts the focus from interaction-level signals to entity-level reliability. Motivated by the empirical observation that training loss correlates with noise, EARD quantifies user and item reliability via their average training losses as a proxy for reputation, and integrates these entity-level factors with interaction-level confidence. The framework is model-agnostic, computationally efficient, and requires only two intuitive hyperparameters. Extensive experiments across multiple datasets and backbone models demonstrate that EARD yields substantial improvements over state-of-the-art baselines (e.g., up to 27.01% gain in NDCG@50), while incurring negligible additional computational cost. Comprehensive ablation studies and mechanism analyses further confirm EARD's robustness to hyperparameter choices and its practical scalability. These results highlight the importance of entity-aware reliability modeling for denoising implicit feedback and pave the way for more robust recommendation research.