A regression framework to interpret the robustness of recommender systems against shilling attacks

Collaborative filtering recommender systems (CF-RSs) employ user-item feedback, e.g., ratings, purchases, or reviews, to harmonize similarities among customers and produce personalized lists of products. Being based on the benevolence of other customers, CF-RSs are vulnerable to Shilling Attacks, i.e., fake profiles injected on the platform by adversaries to hack the recommendation outcomes toward a corrupt behavior. While mainly works on shilling attacks have been conducted to propose novel methods, compare recommendation models and outputs with and without defenses, we have found a lack of study on the impact of dataset properties on the CF-RSs robustness. In this work, we present a regression model to test whether dataset characteristics can impact the robustness of CF-RSs under shilling attacks to interpret their efficacy depending on these characteristics. Obtained results can help the system designer understand the cause of CF-RSs performance variations in attack scenarios.