With the increasing abundance of information resources and the development of deep
learning techniques, recommender systems (RSs) based on deep learning have gradually …

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 …

In sequential recommendation, multi-modal information (eg, text or image) can provide a
more comprehensive view of an item's profile. The optimal stage (early or late) to fuse …

User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been
extensively used in recommender systems. Crucial interactive patterns between users and …

Abstract In Sequential Recommenders (SR), encoding and utilizing modalities in an end-to-
end manner is costly in terms of modality encoder sizes. Two-stage approaches can mitigate …

Sequential recommendation methods are crucial in modern recommender systems for their
remarkable capability to understand a user's changing interests based on past interactions …

User behavior sequence modeling plays a significant role in Click-Through Rate (CTR)
prediction on e-commerce platforms. Except for the interacted items, user behaviors contain …

The sequentiality of sequences plays a crucial role in modeling the dynamic evolution of the
user's interests. Sequential recommendation models have significantly improved with the …

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 …

The grid terminal deploys numerous types of communication equipment for the digital
construction of the smart grid. Once communication equipment failure occurs, it might …