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 …

Recommender systems play a crucial role in mitigating the problem of information overload
by suggesting users' personalized items or services. The vast majority of traditional …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

Recommender systems play a crucial role in mitigating the problem of information overload
by suggesting users' personalized items or services. The vast majority of traditional …

Deep learning-based recommender systems (DLRSs) often have embedding layers, which
are utilized to lessen the dimension of categorical variables (eg, user/item identifiers) and …

In recent years, the textile and fashion industries have witnessed an enormous amount of
growth in fast fashion. On e-commerce platforms, where numerous choices are available, an …

Deep recommender systems have achieved promising performance on real-world
recommendation tasks. They typically represent users and items in a low-dimensional …

Designing an effective loss function plays a crucial role in training deep recommender
systems. Most existing works often leverage a predefined and fixed loss function that could …

The recent rapid growth of the Internet content has led to building recommendation systems
that guide users to their needs through an information retrieving process. An expert …