A Comprehensive Study of Shilling Attacks in Recommender Systems
With the abundance of data available, it becomes difficult to distinguish useful information from massive amount of information available. Recommender systems serves the purpose of filtering information to provide relevant information to users that best acknowledge their needs. In order to generate efficient recommendations to its target users, a recommender system may use user data such as user identity, demographic profile, purchase history, rating history, browsing behaviour etc. This may raise security and privacy concerns for a user. The goal of this paper is to address various security and privacy issues in a recommender system. In this paper, we also discuss some of the evaluation metrics for various attack models.
Keywords: Privacy, security, recommender system, shilling attacks.
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ABOUT THE AUTHORS
Tulika Kumari
Assistant Professor, Department of Computer Science, Shaheed Rajguru College of Applied Sciences for Women, University of Delhi, INDIA
Punam Bedi
Professor Department of Computer Science University of Delhi, North Campus New Delhi - 110007, INDIA
Tulika Kumari
Assistant Professor, Department of Computer Science, Shaheed Rajguru College of Applied Sciences for Women, University of Delhi, INDIA
Punam Bedi
Professor Department of Computer Science University of Delhi, North Campus New Delhi - 110007, INDIA
