With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

Recommender systems have become exceptionally widespread in recent years to deal with
the information overload problem by providing personalized recommendations. Multi-criteria …

Recently, artificial intelligence (AI) technology receives extensive attention in the
manufacturing field. As the core technology, it generates considerable interest among smart …

We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender
systems. The J-NCF model applies a joint neural network that couples deep feature learning …

With the growth of online information, varying personalization drifts and volatile behaviors of
internet users, recommender systems are effective tools for information filtering to overcome …

With the recent development of online transactions, recommender systems have
increasingly attracted attention in various domains. The recommender system supports the …

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