This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes
guidelines on how information systems (IS) research could contribute to this research …

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 …

Collaborative filtering recommender systems (CFRSs) have already been proved effective to
cope with the information overload problem since they merged in the past two decades …

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 …

The existing unsupervised methods usually require a prior knowledge to ensure the
performance when detecting shilling attacks in collaborative filtering recommender systems …

Online recommender systems have been shown to be vulnerable to group shilling attacks in
which attackers of a shilling group collaboratively inject fake profiles with the aim of …

Collaborative recommender systems are vulnerable to recommendation attack, in which
malicious users insert fake profiles into the rating database in order to bias the systems …

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 …

The existing unsupervised methods for detecting shilling attacks are mostly based on the
rating patterns of users, ignoring the rating behavior difference between genuine users and …