Friday 5 June 2015
shilling attacks on recommender system
Summary
One area of research which has recently gained importance is the security of Recommender Systems. Recommender Systems are widely used to help deal with the problem of information overload, by making personalized recommendations for information, products and services during a live interaction. In recent years, these systems have become a crucial feature in e-commerce sites. Automated Collaborative Filtering (ACF) has been successfully employed in them in order to help users deal with the number of options available to them, by making high quality recommendations. Recommender Systems are not beneficial only to the consumers of the product but also to the retail companies that produce those products, since recommendations of their products will in turn result in increased sales and customer satisfaction. However, the open nature of these systems make them vulnerable to Shilling attacks in which malicious users may influence the system by inserting biased data into the system, in order to push the prediction of some targeted items. Unscrupulous producers attack such systems to have their products recommended more often than those of their competitors. They hire agents called shills that manipulate the system by giving false opinion about the target products and mislead the consumers. Such attacks may lead to erosion of user’s trust in the objectivity and accuracy of the system. This paper focuses on the algorithms being used in recommender systems, effectiveness of the shilling attacks on recommender systems and detectability of these attacks.
Submitted By: Pulkit Jain(9911103514)
Karan Goyal(9911103474)
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