Analysing supervised learning approaches for detecting shilling attacks in collaborative recommendations

Abstract

Collaborative recommendation systems offer users personalized recommendations based on their past interactions and the actions of other users. However, these systems can be compromised by shilling attacks, in which fake feedback and ratings are introduced in order to manipulate the recommendations made by the system. It is important to identify and mitigate these attacks to maintain the reliability and accuracy of the recommendations. There are different ways to deal with shilling attacks, which involve using technology to detect fake ratings and assess the trustworthiness of users. Some solutions include using machine learning to spot patterns, applying filters to weed out fake ratings, using a combination of different filtering techniques to make recommendations, and establishing reputation systems to evaluate the reliability of users and their ratings. This work provides a comprehensive overview of the current methods for detecting shilling attacks in collaborative recommendation systems, including different types of attacks and various detection approaches. It also discusses the limitations and challenges of these approaches and compares their performance.