Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …

Recent advancements of sequential deep learning models such as Transformer and BERT
have significantly facilitated the sequential recommendation. However, according to our …

Online advertising, as a vast market, has gained significant attention in various platforms
ranging from search engines, third-party websites, social media, and mobile apps. The …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

In the mobile Internet era, recommender systems have become an irreplaceable tool to help
users discover useful items, thus alleviating the information overload problem. Recent …

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential
recommender systems by achieving state-of-the-art recommendation performance on …

Session-based recommendation plays a central role in a wide spectrum of online
applications, ranging from e-commerce to online advertising services. However, the majority …

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of
recommending the next item based on a sequence of recorded user behaviors, where …

Owing to its nature of scalability and privacy by design, federated learning (FL) has received
increasing interest in decentralized deep learning. FL has also facilitated recent research on …