Towards Robust Recommendation: A Review and an Adversarial Robustness Evaluation Library

TL;DR

This paper reviews the robustness challenges in recommender systems, categorizes types of robustness, and introduces ShillingREC, an evaluation library for adversarial robustness assessment.

Contribution

It provides a comprehensive survey of recommender system robustness and introduces ShillingREC, a library for evaluating adversarial attacks and defenses.

Findings

Analysis of adversarial attack and defense methods

Evaluation of basic attack models using ShillingREC

Discussion of future research directions in robustness

Abstract

Recently, recommender system has achieved significant success. However, due to the openness of recommender systems, they remain vulnerable to malicious attacks. Additionally, natural noise in training data and issues such as data sparsity can also degrade the performance of recommender systems. Therefore, enhancing the robustness of recommender systems has become an increasingly important research topic. In this survey, we provide a comprehensive overview of the robustness of recommender systems. Based on our investigation, we categorize the robustness of recommender systems into adversarial robustness and non-adversarial robustness. In the adversarial robustness, we introduce the fundamental principles and classical methods of recommender system adversarial attacks and defenses. In the non-adversarial robustness, we analyze non-adversarial robustness from the perspectives of data sparsity, natural noise, and data imbalance. Additionally, we summarize commonly used datasets and evaluation metrics for evaluating the robustness of recommender systems. Finally, we also discuss the current challenges in the field of recommender system robustness and potential future research directions. Additionally, to facilitate fair and efficient evaluation of attack and defense methods in adversarial robustness, we propose an adversarial robustness evaluation library--ShillingREC, and we conduct evaluations of basic attack models and recommendation models. ShillingREC project is released at https://github.com/chengleileilei/ShillingREC.