Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

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 …

Recently, significant progress has been made in sequential recommendation with deep
learning. Existing neural sequential recommendation models usually rely on the item …

The next location recommendation is at the core of various location-based applications.
Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical …

Recently, graph neural networks (GNNs) have gained increasing popularity due to their
convincing performance in various applications. Many previous studies also attempted to …

Modeling customer shop** intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

There is an increasing attention on next-item recommendation systems to infer the dynamic
user preferences with sequential user interactions. While the semantics of an item can …

In recent years, there are a large number of recommendation algorithms proposed in the
literature, from traditional collaborative filtering to deep learning algorithms. However, the …

Predicting the next interaction of a short-term interaction session is a challenging task in
session-based recommendation. Almost all existing works rely on item transition patterns …

Next basket recommendation aims to infer a set of items that a user will purchase at the next
visit by considering a sequence of baskets he/she has purchased previously. This task has …