BERT4Rec is an effective model for sequential recommendation based on the Transformer
architecture. In the original publication, BERT4Rec claimed superiority over other available …

Sequential recommendation aims to recommend the next item that matches a user'sinterest,
based on the sequence of items he/she interacted with before. Scrutinizingprevious studies …

In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …

Side information fusion for sequential recommendation (SR) aims to effectively leverage
various side information to enhance the performance of next-item prediction. Most state-of …

The self-attention mechanism, which equips with a strong capability of modeling long-range
dependencies, is one of the extensively used techniques in the sequential recommendation …

Sequential recommendation is an important task to predict the next-item to access based on
a sequence of interacted items. Most existing works learn user preference as the transition …

While some powerful neural network architectures (eg, Transformer, Graph Neural
Networks) have achieved improved performance in sequential recommendation with high …

Leading sequential recommendation (SeqRec) models adopt empirical risk minimization
(ERM) as the learning framework, which inherently assumes that the training data (historical …

Recommender Systems (RS) play an integral role in enhancing user experiences by
providing personalized item suggestions. This survey reviews the progress in RS inclusively …