In recent years, sequential recommender systems (SRSs) and session-based recommender
systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but …

Widely applied in today's recommender systems, sequential recommendation predicts the
next interacted item for a given user via his/her historical item sequence. However …

Sequential recommendation is one of the hot research topics in recent years. Various
sequential recommendation models have been proposed, of which Self-Attention (SA) …

Sequential recommenders capture dynamic aspects of users' interests by modeling
sequential behavior. Previous studies on sequential recommendations mostly aim to identify …

The changing preferences of users towards items trigger the emergence of session-based
recommender systems (SBRSs), which aim to model the dynamic preferences of users for …

Next-item recommendation has been a hot research, which aims at predicting the next
action by modeling users' behavior sequences. While previous efforts toward this task have …

Contrastive Learning (CL) performances as a rising approach to address the challenge of
sparse and noisy recommendation data. Although having achieved promising results, most …

The recent boom in online courses has necessitated personalized online course
recommendation. Modelling the learning sequences of users is key for course …

Sequential recommendation systems utilize the sequential interactions of users with items
as their main supervision signals in learning users' preferences. However, existing methods …

A sequential recommendation has become a hot research topic, which seeks to predict the
next interesting item for each user based on his action sequence. While previous methods …