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 …

Collaborative Filtering (CF) is one of the best performing and most widely used approaches
for recommender systems. Although significant progress has been made in this area, current …

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 …

Collaborative filtering aims to make use of users' feedbacks to improve the recommendation
performance, which has been deployed in various industry recommender systems. Some …

So far, many researchers have worked on recommender systems using users' implicit
feedback, since it is difficult to collect explicit item preferences in most applications. Existing …

Collaborative filtering is a relatively young algorithmic approach, which already found its
way into many commercial applications and established itself as a prime component of …

We introduce TagMF, a model-based Collaborative Filtering method that aims at increasing
transparency and offering richer interaction possibilities in current Recommender Systems …

Collaborative filtering plays the key role in recent recommender systems. It uses a user-item
preference matrix rated either explicitly (ie, explicit rating) or implicitly (ie, implicit feedback) …

Recommender systems are promising for providing personalized favorite services.
Collaborative filtering (CF) technologies, making prediction of users' preference based on …

Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …