Abstract In the Big Data Era, recommender systems perform a fundamental role in data
management and information filtering. In this context, Collaborative Filtering (CF) persists as …

Collaborative filtering (CF) is a widely used approach in recommender systems to solve
many real-world problems. Traditional CF-based methods employ the user-item matrix …

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

Owing to the enormous growth in information over the past few decades, the world has
become a global village. The recommendation system remains the most widely used type of …

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 …

Collaborative filtering (CF) has been widely employed within recommender systems to solve
many real-world problems. Learning effective latent factors plays the most important role in …

This paper provides an innovative deep learning architecture to improve collaborative
filtering results in recommender systems. It exploits the potential of the reliability concept to …

Recommendation services become a critical and hot research topic for researchers. A
recommendation agent that automatically suggests products to users according to their …

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

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 …