Sequential dynamics are a key feature of many modern recommender systems, which seek
to capture the'context'of users' activities on the basis of actions they have performed recently …

Sequential recommender systems seek to exploit the order of users' interactions, in order to
predict their next action based on the context of what they have done recently. Traditionally …

The capability of extracting sequential patterns from the user-item interaction data is now
becoming a key feature of recommender systems. Though it is important to capture the …

User preferences are usually dynamic in real-world recommender systems, and a user» s
historical behavior records may not be equally important when predicting his/her future …

Since sequential information plays an important role in modeling user behaviors, various
sequential recommendation methods have been proposed. Methods based on Markov …

Predicting users' preferences based on their sequential behaviors in history is challenging
and crucial for modern recommender systems. Most existing sequential recommendation …

Sequential recommendation methods play a crucial role in modern recommender systems
because of their ability to capture a user's dynamic interest from her/his historical inter …

Sequential recommendation models the dynamics of a user's previous behaviors in order to
forecast the next item, and has drawn a lot of attention. Transformer-based approaches …

Recently, self-attention based models have achieved state-of-the-art performance in
sequential recommendation task. Following the custom from language processing, most of …

With great value in real applications, sequential recommendation aims to recommend users
the personalized sequential actions. To achieve better performance, it is essential to …