Robust Recommendation

Robust recommendation focuses on developing recommender systems that are resilient to various forms of noise and attacks, aiming to provide reliable and unbiased recommendations despite data imperfections. Current research emphasizes mitigating biases stemming from popularity shifts, adversarial attacks (including data poisoning), and hardware errors, often employing techniques like adversarial training, cooperative model training, and debiasing strategies within collaborative filtering and graph neural network frameworks. These advancements are crucial for improving the trustworthiness and fairness of recommender systems, impacting both the theoretical understanding of robust machine learning and the practical deployment of reliable recommendation services across various applications.

Papers

January 23, 2024

Securing Recommender System via Cooperative Training
Qingyang Wang, Chenwang Wu, Defu Lian, Enhong Chen
Privacy Preserving Collaborative Training Robust Recommendation Training Set Attack

October 16, 2023

Robust Collaborative Filtering to Popularity Distribution Shift
An Zhang, Wenchang Ma, Jingnan Zheng, Xiang Wang, Tat-seng Chua
Popularity Bias Popularity Score Robust Recommendation Dynamic Content Popularity Content Popularity

September 29, 2023

Toward Robust Recommendation via Real-time Vicinal Defense
Yichang Xu, Chenwang Wu, Defu Lian
Recommender System Real Time Evasion Attack Robust Recommendation Robust Learning Method

August 20, 2023

Adversarial Collaborative Filtering for Free
Huiyuan Chen, Xiaoting Li, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Mahashweta Das, Hao Yang
Adversarial Training Adversarial Perturbation Adversarial Learning Robust Recommendation

July 17, 2023

Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors
Dongning Ma, Xun Jiao, Fred Lin, Mengshi Zhang, Alban Desmaison, Thomas Sellinger, Daniel Moore, Sriram Sankar
Native Robustness Pytorch Model Error Mitigation Hardware Error Robust Recommendation Deep Recommendation

March 26, 2023

PORE: Provably Robust Recommender Systems against Data Poisoning Attacks
Jinyuan Jia, Yupei Liu, Yuepeng Hu, Neil Zhenqiang Gong
Recommender System Poisoning Attack Data Poisoning Attack Pore Space Robust Recommendation

February 11, 2023

Adversarial Online Collaborative Filtering
Stephen Pasteris, Fabio Vitale, Mark Herbster, Claudio Gentile, Andre' Panisson
Collaborative Filtering Regret Guarantee User Item Robust Recommendation Knowledge Constraint

November 8, 2022

Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection
Yuni Lai, Yulin Zhu, Wenqi Fan, Xiaoge Zhang, Kai Zhou
Adversarial Robustness Recommender System GNN Based Node Injection Robust Recommendation

October 25, 2022

Towards Robust Recommender Systems via Triple Cooperative Defense
Qingyang Wang, Defu Lian, Chenwang Wu, Enhong Chen
Recommender System Model Generalization Better Robustness Robust Recommendation

October 21, 2022

Triplet Losses-based Matrix Factorization for Robust Recommendations
Flavio Giobergia
Recommender System Evaluation Metric Robust Representation Triplet Loss Robust Recommendation

September 28, 2022

Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design
Mirae Kim, Simon Woo
Absolute Stance Bias Recommendation System New Attack Recommendation Model Discussion Paper Robust Recommendation User Bias

December 21, 2021

Robust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors
Jie Chen, Lifen Jiang, Chunmei Ma, Huazhi Sun
Recommender System Mixed Effect Implicit Feedback Preference Change Robust Recommendation Unexpected Behavior

December 2, 2021

Learning Robust Recommender from Noisy Implicit Feedback
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua
Implicit Feedback Robust Recommendation Noisy Feedback Learning Based Recommender System DeNoising Training