This code is Python implementation of the paper "Sequential Attack Detection in Recommender Systems".
The framework consists of a latent variable model, which is trained given the rating data and user/item attributes, and a CUSUM-like sequential detector to test newly registered users to detect shilling attacks by exploiting the uni-variate statistics from the latent space.
Movielens 1M dataset is provided for a demonstration.