The web contains a huge volume of data, and it's populating every moment to the point that
human beings cannot deal with the vast amount of data manually or via traditional tools …

Collaborative filtering is a fundamental technique in recommender systems, for which
memory-based and matrix-factorization-based collaborative filtering are the two types of …

A major challenge in collaborative filtering based recommender systems is how to provide
recommendations when rating data is sparse or entirely missing for a subset of users or …

Recommender systems, which provide users with recommendations of content suited to
their needs, have received great attention in today's online business world. However, most …

Collaborative Filtering (CF) algorithms have been widely used to provide personalized
recommendations in e-commerce websites and social network applications. Among them …

One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender
system settings. Typical characteristics include: Only positive examples can be observed …

Abstract Recommendation Systems (RSs) are becoming tools of choice to select the online
information relevant to a given user. Collaborative Filtering (CF) is the most popular …

The collaborative filtering (CF) using known user ratings of items has proved to be effective
for predicting user preferences in item selection. This thriving subfield of machine learning …

The recommender systems aim to predict potential demands of users by analyzing their
preferences and provide personalized recommendation services. User preferences can be …

As a result of a huge volume of implicit feedback such as browsing and clicks, many
researchers are involving in designing recommender systems (RSs) based on implicit …