Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

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

Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …

Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

Sequential recommendation problems have received increasing attention in research during
the past few years, leading to the inception of a large variety of algorithmic approaches. In …

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 …

The design of algorithms that generate personalized ranked item lists is a central topic of
research in the field of recommender systems. In the past few years, in particular …

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 are nowadays successfully used by all major web sites—from e-
commerce to social media—to filter content and make suggestions in a personalized way …