Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …

Transformer-based sequential recommendation (SR) has been booming in recent years,
with the self-attention mechanism as its key component. Self-attention has been widely …

As its availability and generality in online services, implicit feedback is more commonly used
in recommender systems. However, implicit feedback usually presents noisy samples in real …

Sequential recommendation systems fundamentally rely on users' historical interaction
sequences, which are often contaminated by noisy interactions. Identifying these noisy …

Sequential recommender systems aim to recommend the next items in which target users
are most interested based on their historical interaction sequences. In practice, historical …

Sequential recommender systems (SRSs) are typically trained to predict the next item as the
target given its preceding (and succeeding) items as the input. Such a paradigm assumes …

In recommendation systems, users frequently engage in multiple types of behaviors, such as
clicking, adding to a cart, and purchasing. However, with diversified behavior data, user …

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her
historical interaction sequences. Recently, many research efforts have been devoted to …

Most sequential recommendation systems (SRSs) predict the next item as the target for
users given its preceding items as input, assuming the target is definitely related to its input …