A REVIEW OF ATTACKS AND ITS DETECTION ATTRIBUTES ON COLLABORATIVE RECOMMENDER SYSTEMS

Vishal Gupta, Saakshi Saakshi Kapoor, Rohit Kumar

Abstract


Today, there is lots of information available over the Internet but it’s very difficult to filter out the required information from this overload of information. Thus a solution to this problem, came as “Recommender Systems”, they can predict outcomes according to user’s interests. Although Recommender Systems are very effective and useful for users but the mostly used type of Recommender System i.e. Collaborative Filtering Recommender System suffers from shilling/profile injection attacks in which fake profiles are inserted into the database in order to bias its output. This paper is aimed at discussing various attacks that can affect Recommender Systems and the attributes that are used for the detection of these attacks.

Keywords


Recommender Systems, Shilling attacks; Generic attributes; Model specific attributes

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DOI: https://doi.org/10.26483/ijarcs.v8i7.4550

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