Recommender system is an information filtering tool used to alleviate information overload
for users on the web. Collaborative filtering recommends items to users based on their …

Most collaborative filtering algorithms are based on certain statistical models of user
interests built from either explicit feedback (eg: ratings, votes) or implicit feedback (eg: clicks …

In recent years, collaborative filtering (CF) techniques have become one of the most
popularly used techniques for providing personalized services to users. CF techniques …

Collaborative filtering aims at predicting a user's interest for a given item based on a
collection of user profiles. This article views collaborative filtering as a problem highly …

Memory-based methods for collaborative filtering predict new ratings by averaging
(weighted) ratings between, respectively, pairs of similar users or items. In practice, a large …

The task of collaborative filtering is to predict the preferences of an active user for unseen
items given preferences of other users. These preferences are typically expressed as …

With the rapid development and application of the mobile Internet, huge amounts of user
data are generated and collected every day. How to take full advantages of these ubiquitous …

Due to the explosion of available information on the Internet, the need for effective means of
accessing and processing them has become vital for everyone. Recommender systems …

Collaborative filtering (CF) is the most popular approach in personalized recommender
systems. Although CF approaches have successfully been used and have the advantage in …

Latent factor models have been used widely in collaborative filtering based recommender
systems. In recent years, deep learning has been successful in solving a wide variety of …