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 …

In the era of online business, many e-commerce sites have evolved which recommend items
according to one's needs and interests. Plenty of data is available to be processed to make …

Nowadays, due to the increasing amount of data, the use of recommender systems has
increased. Therefore, the quality of the recommendations for the users of these systems is …

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 degree of openness of the socially aware recommendation systems and the possibility
of the attackers injecting vast numbers of fake profiles biases the prediction of the system …

Reliability of a recommender system is extremely substantial for the continuity of the system.
Malicious users may harm the reliability of predictions by injecting fake profiles called …

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 …

With recent advances in e-commerce platforms, the information overload has grown due to
increasing number of users, rapid generation of data and items in the recommender system …

The accuracy and diversity of recommendation algorithms have always been the research
hotspot of recommender systems. A good recommender system should not only have high …

A recommendation system is based on the user and the items, providing appropriate items
to the user and effectively helping the user to find items that may be of interest. The most …