Ensemble approach to detect profile injection attack in recommender system

Recommender systems apply knowledge discovery techniques to specific problem of making personalized recommendation for the products or services to the users. The huge growth in the information and the number of visitors to the web sites especially on e-commerce in last few years creates some challenges for recommender systems. E-commerce recommender systems are highly vulnerable to the profile injection attacks, involving insertion of fake profiles into the system to influence the recommendations made to the users. Prior work has shown that even a small number of malicious profiles can bias the system significantly. In this paper, we compare six machine learning algorithms and based on their performance we build our ensemble model and measure its performance in the detection of profile injection attacks.