# Defending Collaborative Filtering Recommenders via Adversarial Robustness Based Edge Reweighting

arXiv: https://arxiv.org/abs/2412.10850 · HTML: https://mlsanity.com/paper/2412.10850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Adversarial Training and Robustness for Multiple Perturbations](https://mlsanity.com/paper/1904.13000/) (2019)
- [GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection](https://mlsanity.com/paper/2005.10150/) (2020)
- [Adversarial Personalized Ranking for Recommendation](https://mlsanity.com/paper/1808.03908/) (2018)
- [Data Poisoning Attacks to Deep Learning Based Recommender Systems](https://mlsanity.com/paper/2101.02644/) (2021)
- [Revisiting Adversarially Learned Injection Attacks Against Recommender Systems](https://mlsanity.com/paper/2008.04876/) (2020)
- [SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation](https://mlsanity.com/paper/2102.03716/) (2021)
- [Robust Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2309.02057/) (2023)
- [Re-scale AdaBoost for Attack Detection in Collaborative Filtering Recommender Systems](https://mlsanity.com/paper/1506.04584/) (2015)

## Abstract (arXiv)

User based collaborative filtering (CF) relies on a user and user similarity graph, making it vulnerable to profile injection (shilling) attacks that manipulate neighborhood relations to promote (push) or demote (nuke) target items. In this work, we propose an adversarial robustness based edge reweighting defense for CF. We first assign each user and user edge a non robustness score via spectral adversarial robustness evaluation, which quantifies the edge sensitivity to adversarial perturbations. We then attenuate the influence of non robust edges by reweighting similarities during prediction. Extensive experiments demonstrate that the proposed method effectively defends against various types of attacks.