Recommender systems are efficient tools for filtering online information, which is
widespread owing to the changing habits of computer users, personalization trends, and …

Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

For the past few years most published research on recommendation algorithms has been
based on deep learning (DL) methods. Following common research practices in our field …

Recommendation is crucial in both academia and industry, and various techniques are
proposed such as content-based collaborative filtering, matrix factorization, logistic …

Deep neural networks have been extensively employed in many applications such as
natural language processing and computer vision. They have attracted a lot of attention in …

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

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 …

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 …

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