Recommender systems employ recommendation algorithms to predict users' preferences to
items. These preferences are often represented as numerical ratings. However, existing …

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 …

Recommender systems have become extremely common in recent years, and are applied in
a variety of domains. Existing recommender systems exhibit two major limitations:(1) Privacy …

In online social communities, many recommender systems use collaborative filtering, a
method that makes recommendations based on what are liked by other users with similar …

We often make decisions on the things we like, dislike, or even don't care about. However,
taking the right decisions becomes relatively difficult from a variety of items from different …

Recommender systems can help users to find interesting content, often based on similarity
with other users. However, studies have shown that in some cases familiarity gives …

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 …

Recommender systems are effective mechanisms for recommendations about what to
watch, read, or taste based on user ratings about experienced products or services. To …

Data collected for recommendation purposes might be distributed among various e‐
commerce sites, which can collaboratively provide more accurate predictions. However …

The prediction of the rating that a user is likely to give to an item, can be derived from the
ratings of other items given by other users, through collaborative filtering (CF). However, CF …