The sequential recommender (SR) system is a crucial component of modern recommender
systems, as it aims to capture the evolving preferences of users. Significant efforts have …

Sequential recommendation (SRS) has become the technical foundation in many
applications recently, which aims to recommend the next item based on the user's historical …

Sequential recommendation is dedicated to offering items of interest for users based on their
history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items …

Sequential recommendation typically utilizes deep neural networks to mine rich information
in interaction sequences. However, existing methods often face the issue of insufficient …

Sequential Recommender Systems (SRS) are extensively applied across various domains
to predict users' next interaction by modeling their interaction sequences. However, these …

Recommender systems are an important tool for information retrieval, which can aid in the
solution of the issue of information overload. Recently, contrastive learning has shown …

User modeling, which learns to represent users into a low-dimensional representation space
based on their past behaviors, got a surge of interest from the industry for providing …

Sequential recommender systems (SRS) aim to predict users' subsequent choices based on
their historical interactions and have found applications in diverse fields such as e …

Recently, the item textual information has been exploited with pre-trained language models
(PLMs) to enrich the representations of tail items. The underlying idea is to align the hot …

Large vision models (LVMs) and large language models (LLMs) are becoming cutting-edge
for sequential recommendation, given their success in broad applications. Despite their …