The popularity of online recommender systems has soared; they are deployed in numerous
websites and gather tremendous amounts of user data that are necessary for …

In many online applications, the range of content that is offered to users is so wide that a
need for automated recommender systems arises. Such systems can provide a personalized …

With increasing need for preserving confidential data while providing recommendations,
privacy-preserving collaborative filtering has been receiving increasing attention. To make …

In an implicit authentication system, a user profile is used as an additional factor to
strengthen the authentication of mobile users. The profile consists of features that are …

Nowadays, recommender systems have been increasingly used by companies to improve
their services. Such systems are employed by companies in order to satisfy their existing …

The increasing popularity of the micro-blogging sites like Twitter, which facilitates users to
exchange short messages (aka tweets) is an impetus for data analytics tasks for varied …

Rating-based collaborative filtering (CF) enables the prediction of the rating that a user will
give to an item, based on the ratings of other items given by other users. However, doing this …

Rating-based collaborative filtering (CF) predicts the rating that a user will give to an item,
derived from the ratings of other items given by other users. Such CF schemes utilise either …

Abstract Recommender systems use, amongst others, a mechanism called collaborative
filtering (CF) to predict the rating that a user will give to an item given the ratings of other …

Recommender systems typically use collaborative filtering to make sense of huge and
growing volumes of data. An emerging trend in industry has been to use public clouds to …