Personalized session-based recommendation systems aim to predict the next items that a
user would be interested in based on previously recorded user interactions. The task …

Graph convolutional neural networks have attracted increasing attention in recommendation
system fields because of their ability to represent the interactive relations between users and …

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 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 …

The main idea of a session-based recommendation system is to model the user's historical
click sequence and then summarize user preferences and predict the items the user will …

Session-based recommendations (SBR) have attracted much attention because of their high
commercial value. SBR aims to predict a user's next click based on the sequence of …

Graph neural network (GNN)-based models have achieved state-of-the-art performance in
session-based recommendation (SBR). In our research, we observe that the subsequence …

Session-based recommender systems (SBRS) play a crucial role in predicting the next click
of a user from anonymous session data on numerous online platforms such as e-commerce …

Recently, session-based recommendation system (SRS) based on anonymous sessions
has attracted increasing attention for practical applications. Most existing methods are …

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