Recommendation systems have been widely used by commercial service providers for giving suggestions to users based on their previous behaviors. While a large portion of users faithfully express their opinions, some malicious users add noisy ratings in order to change the overall ratings of a specific group of items. The presence of noise can add bias to recommendations, leading to instabilities in estimation and prediction. Although the robustness of different recommendation systems has been extensively studied, designing a robust recommendation system remains a significant challenge as detecting malicious users is computationally expensive. In this work, we propose Chiron, a fast and robust hybrid recommendation system that is not only faster than most state-of-the-art methods, but is also resistant to manipulation by malicious users.
['Saber Shokat Fadaee', 'Hossein Azari Soufiani', 'Ravi Sundaram']
Chiron: A Robust Recommendation System