Advancing Recommender Systems by mitigating Shilling attacks
This addresses security vulnerabilities in recommender systems for users and platforms, but it is incremental as it builds on existing detection methods.
The paper tackles the problem of shilling attacks in collaborative filtering recommender systems, proposing an algorithm to accurately detect malicious profiles and studying their impact on recommendations.
Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as 'shilling attacks' are performed to push an item or nuke an item in the system. This paper proposes an algorithm to detect such shilling profiles in the system accurately and also study the effects of such profiles on the recommendations.