Session-based recommendation is a challenging task. Without access to a user's historical
user-item interactions, the information available in an ongoing session may be very limited …

Recent years have witnessed the remarkable success of recommendation systems (RSs) in
alleviating the information overload problem. As a new paradigm of RSs, session-based …

Session-based recommendation aims to predict a user's next action based on previous
actions in the current session. The major challenge is to capture authentic and complete …

Session-based recommendation (SBR), which mainly relies on a user's limited interactions
with items to generate recommendations, is a widely investigated task. Existing methods …

Sequential recommendation is the task of predicting the next items for users based on their
interaction history. Modeling the dependence of the next action on the past actions …

Recently, graph neural networks (GNNs) have achieved promising results in session-based
recommendation. Existing methods typically construct a local session graph and a global …

Considering sessions as directed subgraphs, graph neural networks (GNNs) are supposed
to be capable of capturing the complex dependencies among items and suitable for session …

The changing preferences of users towards items trigger the emergence of session-based
recommender systems (SBRSs), which aim to model the dynamic preferences of users for …

Session-based recommender systems (SBRSs) have shown superior performance over
conventional methods. However, they show limited scalability on large-scale industrial …