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 …

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

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 …

Easy internet access and technological advancements have resulted in information overload
and a plethora of options, making decision-making extremely difficult. Recommender …

In the current digital landscape, both information consumers and producers encounter
numerous challenges, underscoring the importance of recommender systems (RS) as a vital …

Recommenders utilize the knowledge discovery-based methods for identifying information
required by the user. The recommender system faces some serious challenges in recent …

The electronic publication market is growing along with the electronic commerce market.
Electronic publishing companies use recommendation systems to increase sales to …

Recommendation systems have been well established to reduce the problem of information
overload and have become one of the most valuable tools applicable to different domains …

Recommender systems are essential engines to deliver product recommendations for e‐
commerce businesses. Successful adoption of recommender systems could significantly …