A state of art survey on shilling attack in collaborative filtering based recommendation system

Abstract

Recommendation system is a special type of information filtering system that attempts to present information/objects that are likely to the interest of user. Any organization, provides correct recommendation is necessary for maintain the trust of their customers. Collaborative filtering based algorithms are most widely used algorithms for recommendation system. However, recommender systems supported collaborative filtering are known to be extremely prone to attacks. Attackers will insert biased profile information or fake profile to have a big impact on the recommendations made. This paper provide survey on effect of shilling attack in recommendation systems, types of attack, knowledge required and existing shilling attack detection methods.