Chiron: A Robust Recommendation System with Graph Regularizer

TL;DR

Chiron is a new recommendation system that combines graph regularization with matrix factorization to enhance robustness against malicious manipulation, showing improved stability and resistance in experiments.

Contribution

The paper introduces Chiron, a hybrid recommendation system that integrates graph regularization with matrix factorization to improve robustness against adversarial attacks.

Findings

Chiron demonstrates strong resistance to manipulation in experiments.

It combines benefits of dimensionality reduction and neighborhood clustering.

Outperforms traditional CF methods in robustness tests.

Abstract

Recommendation systems have been widely used by commercial service providers for giving suggestions to users. Collaborative filtering (CF) systems, one of the most popular recommendation systems, utilize the history of behaviors of the aggregate user-base to provide individual recommendations and are effective when almost all users faithfully express their opinions. However, they are vulnerable to malicious users biasing their inputs in order to change the overall ratings of a specific group of items. CF systems largely fall into two categories - neighborhood-based and (matrix) factorization-based - and the presence of adversarial input can influence recommendations in both categories, leading to instabilities in estimation and prediction. Although the robustness of different collaborative filtering algorithms has been extensively studied, designing an efficient system that is immune to manipulation remains a significant challenge. In this work we propose a novel "hybrid" recommendation system with an adaptive graph-based user/item similarity-regularization - "Chiron". Chiron ties the performance benefits of dimensionality reduction (through factorization) with the advantage of neighborhood clustering (through regularization). We demonstrate, using extensive comparative experiments, that Chiron is resistant to manipulation by large and lethal attacks.