Most online service providers utilize a recommender system to help their customers'
decision making process by producing referrals. If a customer requests a suggestion for a …

Group recommender systems are specialized in suggesting preferable products or services
to a group of users rather than an individual by aggregating personal preferences of group …

Collaborative filtering algorithms unwittingly produce ranked lists where a few popular items
are recommended too frequently while the remaining vast amount of items get not deserved …

Recommender systems are emerging techniques guiding individuals with provided referrals
by considering their past rating behaviors. By collecting multi‐criteria preferences …

Memory-based collaborative filtering schemes are among the most effective
recommendation technologies in terms of prediction quality, despite commonly facing issues …

Collaborative filtering recommendation algorithms are vulnerable against the popularity
bias, including the most popular items repeatedly into the produced ranked lists. However …

The main goal of a group recommender system is to provide appropriate referrals to a group
of users sharing common interests rather than individuals. Such group referrals are …

Recommendation systems have popular methods that help users generate predictions for a
product and create product lists based on users' feedback. The output of such systems can …

Recommender systems provide their users an efficient way to handle information overload
problem by offering personalized suggestions. Traditional recommender systems are based …

Recommender systems offer tailored recommendations by employing various algorithms,
and collaborative filtering is one of the well-known and commonly used of those. A …