A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Graph neural networks in recommender systems: a survey
With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …
alleviate such information overload. Due to the important application value of recommender …
DDGHM: Dual dynamic graph with hybrid metric training for cross-domain sequential recommendation
Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by
modeling how users transit among items. However, the short interaction sequences limit the …
modeling how users transit among items. However, the short interaction sequences limit the …
Cold-start sequential recommendation via meta learner
This paper explores meta-learning in sequential recommendation to alleviate the item cold-
start problem. Sequential recommendation aims to capture user's dynamic preferences …
start problem. Sequential recommendation aims to capture user's dynamic preferences …
[HTML][HTML] Jointly modeling intra-and inter-session dependencies with graph neural networks for session-based recommendations
Recently, graph neural networks (GNNs) have achieved promising results in session-based
recommendation. Existing methods typically construct a local session graph and a global …
recommendation. Existing methods typically construct a local session graph and a global …
Graph neural networks with global noise filtering for session-based recommendation
L Feng, Y Cai, E Wei, J Li - Neurocomputing, 2022 - Elsevier
Session-based recommendation leverages anonymous sessions to predict which item a
user is most likely to click on next. While previous approaches capture items-transition …
user is most likely to click on next. While previous approaches capture items-transition …
Session-based recommendation with hypergraph convolutional networks and sequential information embeddings
Session-based recommendation focuses on predicting the next item that an anonymous
user is most likely to click. Due to its privacy-protecting ability, it is receiving increasing …
user is most likely to click. Due to its privacy-protecting ability, it is receiving increasing …
DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems
In recent years, with the development of Internet technology, recommender systems have
been widely used by virtue of their ability to meet the personalized needs of users. In order …
been widely used by virtue of their ability to meet the personalized needs of users. In order …
Personalized behavior-aware transformer for multi-behavior sequential recommendation
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how
users transit among items. However, SR models that utilize only single type of behavior …
users transit among items. However, SR models that utilize only single type of behavior …
Exploiting explicit and implicit item relationships for session-based recommendation
The session-based recommendation aims to predict users' immediate next actions based on
their short-term behaviors reflected by past and ongoing sessions. Graph neural networks …
their short-term behaviors reflected by past and ongoing sessions. Graph neural networks …