Recommender systems have become an integral part of online services due to their ability to
help users locate specific information in a sea of data. However, existing studies show that …

With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

The internet is the home for huge volumes of useful data that is constantly being created
making it difficult for users to find information relevant to them. Recommendation System is a …

With the development of personalized recommendations, information overload has been
alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of …

In social recommender systems or trust-based recommender systems, malicious users can
bias the recommendations by injecting a large number of fake profiles and by building …

The stability and reliability of filtration and recommender systems are crucial for continuous
operation. The presence of fake profiles, known as “shilling attacks,” can undermine the …

Collaborative recommender systems are vulnerable to shilling attacks. To address this
issue, many methods including supervised and unsupervised have been proposed …

Collaborative filtering (CF) recommender systems have been shown to be vulnerable to
shilling attacks. How to quickly and effectively detect shilling attacks is a key challenge for …

Faced with the evolving attacks in collaborative recommender systems, the conventional
shilling detection methods rely mainly on one kind of user‐generated information (ie, single …

The increasing use of recommender systems as personalization recommendation services
such as Amazon, TripAdvisor, and Yelp, has stressed the demand for secure and usable …