A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
Deep learning, graph-based text representation and classification: a survey, perspectives and challenges
Recently, with the rapid developments of the Internet and social networks, there have been
tremendous increase in the amount of complex-structured text resources. These information …
tremendous increase in the amount of complex-structured text resources. These information …
Fedgnn: Federated graph neural network for privacy-preserving recommendation
Graph neural network (GNN) is widely used for recommendation to model high-order
interactions between users and items. Existing GNN-based recommendation methods rely …
interactions between users and items. Existing GNN-based recommendation methods rely …
Disentangling long and short-term interests for recommendation
Modeling user's long-term and short-term interests is crucial for accurate recommendation.
However, since there is no manually annotated label for user interests, existing approaches …
However, since there is no manually annotated label for user interests, existing approaches …
Personalized news recommendation: Methods and challenges
Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …
information and alleviate information overload. Although it has been extensively studied …
Fairness-aware news recommendation with decomposed adversarial learning
News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …
recommendation models are usually learned from users' news click behaviors. Usually the …
Graph neural news recommendation with unsupervised preference disentanglement
With the explosion of news information, personalized news recommendation has become
very important for users to quickly find their interested contents. Most existing methods …
very important for users to quickly find their interested contents. Most existing methods …
HieRec: Hierarchical user interest modeling for personalized news recommendation
User interest modeling is critical for personalized news recommendation. Existing news
recommendation methods usually learn a single user embedding for each user from their …
recommendation methods usually learn a single user embedding for each user from their …
Self-supervised temporal graph learning with temporal and structural intensity alignment
Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …
tasks with dynamic information, which has recently garnered increasing attention. In contrast …
Feedrec: News feed recommendation with various user feedbacks
Accurate user interest modeling is important for news recommendation. Most existing
methods for news recommendation rely on implicit feedbacks like click for inferring user …
methods for news recommendation rely on implicit feedbacks like click for inferring user …