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 …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …

As an important way to alleviate information overload, a recommender system aims to filter
out irrelevant information for users and provides them items that they may be interested in. In …

User purchasing prediction with multi-behavior information remains a challenging problem
for current recommendation systems. Various methods have been proposed to address it via …

Session-based recommendation predicts the next item with which a user will interact, given
a sequence of her past interactions with other items. This machine learning problem targets …

Graph neural network (GNN) based recommendation models are observed to be more
vulnerable against carefully-designed malicious records injected into the system, ie, shilling …

Incorporating knowledge graphs (KGs) as side information in recommendation has recently
attracted considerable attention. Despite the success in general recommendation scenarios …

Takeaway recommender systems, which aim to accurately provide stores that offer foods
meeting users' interests, have served billions of users in our daily life. Different from …

Graph embedding based retrieval has become one of the most popular techniques in the
information retrieval community and search engine industry. The classical paradigm mainly …