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 …
Self-supervised multi-channel hypergraph convolutional network for social recommendation
Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …
interaction data is sparse in recommender systems. Most existing social recommendation …
Heterogeneous graph contrastive learning for recommendation
Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured
data in recommender systems. However, real-life recommendation scenarios usually involve …
data in recommender systems. However, real-life recommendation scenarios usually involve …
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 …
[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
Federated social recommendation with graph neural network
Recommender systems have become prosperous nowadays, designed to predict users'
potential interests in items by learning embeddings. Recent developments of the Graph …
potential interests in items by learning embeddings. Recent developments of the Graph …
Knowledge-aware coupled graph neural network for social recommendation
Social recommendation task aims to predict users' preferences over items with the
incorporation of social connections among users, so as to alleviate the sparse issue of …
incorporation of social connections among users, so as to alleviate the sparse issue of …
Diffnet++: A neural influence and interest diffusion network for social recommendation
Social recommendation has emerged to leverage social connections among users for
predicting users' unknown preferences, which could alleviate the data sparsity issue in …
predicting users' unknown preferences, which could alleviate the data sparsity issue in …
Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation
Social recommendation aims to fuse social links with user-item interactions to alleviate the
cold-start problem for rating prediction. Recent developments of Graph Neural Networks …
cold-start problem for rating prediction. Recent developments of Graph Neural Networks …