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

Cold start problem for new users and new items is a major challenge facing most
collaborative filtering systems. Existing methods to collaborative filtering (CF) emphasize to …

Collaborative Filtering (CF), one of the most successful technologies among recommender
systems, is a system assisting users to easily find useful information. One notable challenge …

The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …

Abstract One-Class Collaborative Filtering (OCCF) has recently received much attention in
recommendation communities due to their close relationship with real industry problem …

Collaborative filtering (CF), aiming at predicting users' unknown preferences based on
observational preferences from some users, has become one of the most successful …

Abstract Collaborative Filtering with Implicit Feedbacks (eg, browsing or clicking records),
named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing …

The Collaborative Filtering (CF) recommendation algorithm, one of the most popular
algorithms in Recommendation Systems (RS), mainly includes memory-based and model …

A central goal of collaborative filtering (CF) is to rank items by their utilities with respect to
individual users in order to make personalized recommendations. Traditionally, this is often …

Recommender systems which can automatically match users with their potential favorites
usually rely on Collaborative Filtering (CF). Since in real-world applications the data of …