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

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

Large Language Models (LLMs) have demonstrated remarkable performance across
diverse domains, thereby prompting researchers to explore their potential for use in …

Sequential recommender systems seek to exploit the order of users' interactions, in order to
predict their next action based on the context of what they have done recently. Traditionally …

Sequential dynamics are a key feature of many modern recommender systems, which seek
to capture the'context'of users' activities on the basis of actions they have performed recently …

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 …

The emerging topic of sequential recommender systems has attracted increasing attention in
recent years. Different from the conventional recommender systems including collaborative …

Top-N sequential recommendation models each user as a sequence of items interacted in
the past and aims to predict top-N ranked items that a user will likely interact in a» near …

Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short sessions …

Recently, neural networks have been widely used in e-commerce recommender systems,
owing to the rapid development of deep learning. We formalize the recommender system as …