In the era of internet access, recommender systems try to alleviate the difficulty that
consumers face while trying to find items (eg, services, products, or information) that better …

Collaborative filtering recommender systems (CFRSs) have already been proved effective to
cope with the information overload problem since they merged in the past two decades …

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) …

Food recommender-systems are considered an effective tool to help users adjust their
eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food …

Due to the explosion of available information on the Internet, the need for effective means of
accessing and processing them has become vital for everyone. Recommender systems …

Recommender systems are popular tools used in many applications, such as e-commerce, e-
learning, and social networks to help users select their desired items. Collaborative filtering …

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 …

Recommender systems use intelligent algorithms to learn a user's preferences and provide
them relevant suggestions. Lack of sufficient ratings–also known as data sparsity problem …

Recommender systems (RSs) are programs that apply knowledge discovery techniques to
make personalized recommendations for user's information on the web. In online sharing …

Recommender systems attempt to suggest information that is of potential interest to users
hel** them to quickly find information relevant to them. In addition to historical user–item …