The problem of session-aware recommendation aims to predict users' next click based on
their current session and historical sessions. Existing session-aware recommendation …

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 …

Session-based recommendation nowadays plays a vital role in many websites, which aims
to predict users' actions based on anonymous sessions. There have emerged many studies …

Session-based recommendation aims to predict users' next preference based on the
sequence of their own history preferences in a short period. Most state-of-the-art methods …

Session-based recommendation, which aims to match user needs with rich resources based
on anonymous sessions, nowadays plays a critical role in various online platforms (eg …

Session-based recommendation is a challenging task which predicts the next click based on
the short-term behavior of anonymous users. Compared to other recommendation models …

Session-based recommendations aim to predict a user's next click based on the user's
current and historical sessions, which can be applied to shopping websites and APPs …

Session-based recommendation (SBR) aims to predict the next item at a certain time point
based on anonymous user behavior sequences. Existing methods typically model session …

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

Session-based recommendation aims to generate recommendations merely based on the
ongoing session, which is a challenging task. Previous methods mainly focus on modeling …