Abstract Deep Neural Networks (DNN) based collaborative filtering has been successful in
recommending services by effectively generalizing graph-structured data. However, most …

In the recommendation domain, user rating data has high sparsity and the number of
interaction information from each user is very uneven, which brings great technical …

Most collaborative filtering recommendation algorithms rely too much on the user's historical
rating data. However, selection bias is common in explicit feedback data, which makes the …

Graph convolutional collaborative filtering (CF) has recently made remarkable progress. Its
great success is attributed to the embedding propagation mechanism of graph convolutional …

Due to the rapid development of electronic journals, selecting appropriate journals to
publish research papers has become a significant challenge to researchers. Sometimes …

The rapid growth in the number of Web APIs makes it difficult for developers to find the
appropriate ones. To tackle this issue, researchers have created various powerful automatic …

With the rapid development of artificial intelligence technology, commercial robots have
gradually entered our daily lives. In order to promote product dissemination, shopping guide …

The rapid development of web services has made it increasingly challenging for developers
to find desired web services. To address this issue, researchers have developed various …

Deep learning on graphs and especially graph convolutional networks (GCNs) have shown
superior performance in collaborative filtering. Most GCNs have a message-passing …

Recommendation systems provide personalized products or services to online users by
mining their past preferences. Collaborative filtering is a popular recommendation technique …