Deep learning models on graphs have achieved remarkable performance in various graph
analysis tasks, eg, node classification, link prediction, and graph clustering. However, they …

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative
filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …

Inductive representation learning on temporal graphs is an important step toward salable
machine learning on real-world dynamic networks. The evolving nature of temporal dynamic …

Federated learning was proposed with an intriguing vision of achieving collaborative
machine learning among numerous clients without uploading their private data to a cloud …

Latent user representations are widely adopted in the tech industry for powering
personalized recommender systems. Most prior work infers a single high dimensional …

Personalized recommendation benefits users in accessing contents of interests effectively.
Current research on recommender systems mostly focuses on matching users with proper …

Recommender Systems have been playing essential roles in e-commerce portals. Existing
recommendation algorithms usually learn the ranking scores of items by optimizing a single …

Personalized recommendations are the backbone machine learning (ML) algorithm that
powers several important application domains (eg, ads, e-commerce, etc) serviced from …

The emergence of various disruptive technologies such as big data, Internet of Things, and
artificial intelligence have instigated our society to generate enormous volumes of data. The …

Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender
systems. This task is deemed quite challenging under industrial setting with two major …