Sequential Recommendation aims to predict the next item by mining out the dynamic
preference from user previous interactions. However, most methods represent each item as …

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 …

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

Sequential recommendation (SR) aims to model user preferences by capturing behavior
patterns from their item historical interaction data. Most existing methods model user …

Sequential recommender systems (SRS) aim to infer the users' preferences from their
interaction history and predict items that will be of interest to the users. The majority of SRS …

Sequential recommendation basically aims to capture user evolving preference. Intuitively, a
user interacts with an item usually because of some specific feature, and user evolving …

The chronological order of user-item interactions is a key feature in many recommender
systems, where the items that users will interact may largely depend on those items that …

Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial
Network (GAN), have been successfully applied in sequential recommendation. These …

Recently, contrastive learning for sequential recommendation has demonstrated its powerful
ability to learn high-quality user representations. However, constructing augmented samples …

Contrastive learning has been effectively applied to alleviate the data sparsity issue and
enhance recommendation performance. The majority of existing methods employ random …