Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
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 …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Learning intents behind interactions with knowledge graph for recommendation
Knowledge graph (KG) plays an increasingly important role in recommender systems. A
recent technical trend is to develop end-to-end models founded on graph neural networks …
recent technical trend is to develop end-to-end models founded on graph neural networks …
Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but
the unique data processing and evaluation setups used by each work obstruct a full …
the unique data processing and evaluation setups used by each work obstruct a full …
A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …
understanding, research in recommendation has shifted to inventing new recommender …
A survey on knowledge graph-based recommender systems
To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …
applications, recommender systems have been developed to model users' preferences …
Learning knowledge graph embedding with heterogeneous relation attention networks
Knowledge graph (KG) embedding aims to study the embedding representation to retain the
inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …
inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …
Multi-level cross-view contrastive learning for knowledge-aware recommender system
Knowledge graph (KG) plays an increasingly important role in recommender systems.
Recently, graph neural networks (GNNs) based model has gradually become the theme of …
Recently, graph neural networks (GNNs) based model has gradually become the theme of …
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 …