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 …

Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

摘要深度学习是机器学习领域一个重要研究方向, 近年来在图像处理, 自然语言理解,
语音识别和在线广告等领域取得了突破性进展. 将深度学习融入推荐系统中 …

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node
information and topological structure, have been demonstrated to be powerful in learning on …

Recommender systems have become prosperous nowadays, designed to predict users'
potential interests in items by learning embeddings. Recent developments of the Graph …

Precise user and item embedding learning is the key to building a successful recommender
system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item …

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative
filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the …

Due to the flexibility in modelling data heterogeneity, heterogeneous information network
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …

We study the problem of representation learning in heterogeneous networks. Its unique
challenges come from the existence of multiple types of nodes and links, which limit the …