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 …

Large-scale industrial recommender systems are usually confronted with computational
problems due to the enormous corpus size. To retrieve and recommend the most relevant …

Rating-based methods (eg, collaborative filtering) in recommendation can explicitly model
users and items from their rating patterns, nevertheless suffer from the natural data sparsity …

Feature selection plays an impactful role in deep recommender systems, which selects a
subset of the most predictive features, so as to boost the recommendation performance and …

Existing benchmark datasets for recommender systems (RS) either are created at a small
scale or involve very limited forms of user feedback. RS models evaluated on such datasets …

Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

Identifying user preferences is a complex operation, which makes its automa-tion
challenging, and existing recommendation systems that rely on one of the parameters …

Researchers and data scientists have developed different Recommender System Algorithms
such as Content-Based and Collaborative-Based in order to filter a large amount of …

We propose a novel deep learning hybrid recommender system to address the gaps in
Collaborative Filtering systems and achieve the state-of-the-art predictive accuracy using …

Recommender systems attempt to provide effective suggestions to each user based on their
interests and behaviors. These recommendations usually match the personal user …