Multi-stage ranking pipelines have been a practical solution in modern search systems,
where the first-stage retrieval is to return a subset of candidate documents and latter stages …

Abstract Representation learning over graph structured data has been mostly studied in
static graph settings while efforts for modeling dynamic graphs are still scant. In this paper …

Despite the importance and abundance of temporal knowledge graphs, most of the current
research has been focused on reasoning on static graphs. In this paper, we study the …

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-
ranging applications spanning machine learning, computer vision, natural language …

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility
of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a …

Network alignment plays an important role in a variety of applications. Many traditional
methods explicitly or implicitly assume the alignment consistency which might suffer from …

Help-seeking is a valuable practice in online discussion forums. However, the
asynchronicity and information overload of online discussion forums have made it …

In the era of online business, many e-commerce sites have evolved which recommend items
according to one's needs and interests. Plenty of data is available to be processed to make …

In this paper, we study the problem of blood glucose forecasting and provide a deep
personalized solution. Predicting blood glucose level in people with diabetes has significant …

Network representation learning (NRL) aims at modeling network graph by encoding
vertices and edges into a low-dimensional space. These learned representations can be …