Privacy-Aware Detection of Shilling Profiles on Arbitrarily Distributed Recommender Systems
نویسندگان
چکیده
منابع مشابه
Privacy, Shilling, and The Value of Information in Recommender Systems
Recommender systems are an increasingly popular tool used by many consumers to help deal with information overload in today’s marketplace. At the cost of some personal information, these systems are able to personalize a user’s online experience and guide them toward making better decisions. This paper examines two issues relating to privacy in recommender systems: the value of information and ...
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Collaborative filtering based recommender system is prone to shilling attacks because of its open nature. Shillers inject pseudonomous profiles in the system’s database with the intent of manipulating the recommendations to their benefits. Prior study has shown that the system’s behavior can be easily influenced by even a less number of shilling profiles. In this paper, we simulated various att...
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Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...
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Privacy is an important issue in Context-aware recommender systems (CARSs). In this paper, we propose a privacy-preserving CARS in which a user can limit the contextual information submitted to the server without sacrificing a significant recommendation accuracy. Specifically, for users, we introduce a client-side algorithm that the user can employ to generalize its context to some extent, in o...
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Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2902042