Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

Recently, significant progress has been made in sequential recommendation with deep
learning. Existing neural sequential recommendation models usually rely on the item …

The problem of session-based recommendation aims to predict user actions based on
anonymous sessions. Previous methods model a session as a sequence and estimate user …

Recently, graph neural networks (GNNs) have gained increasing popularity due to their
convincing performance in various applications. Many previous studies also attempted to …

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 …

Predicting users' actions based on anonymous sessions is a challenging problem in web-
based behavioral modeling research, mainly due to the uncertainty of user behavior and the …

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 …