Quick and accurate attack detection in recommender systems through user attributes

Malicious profiles have been a credible threat to collaborative recommender systems. Attackers provide fake item ratings to systematically manipulate the platform. Attack detection algorithms can identify and remove such users by observing rating distributions. In this study, we aim to use the user attributes as an additional information source to improve the accuracy and speed of attack detection. We propose a probabilistic factorization model which can embed mixed data type user attributes and observed ratings into a latent space to generate anomaly statistics for new users. To identify the persistent outliers in the system, we also propose a sequential attack detection algorithm to enable quick and accurate detection based on the probabilistic model learned from genuine users. The proposed model demonstrates significant improvements in both accuracy and speed when compared to baseline algorithms on a popular benchmark dataset.