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

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 …

Sequential recommendation aims to capture users' dynamic preferences, in which data
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …

The marriage of federated learning and recommender system (FedRec) has been widely
used to address the growing data privacy concerns in personalized recommendation …

Modern sociology has profoundly uncovered many convincing social criteria for behavioral
analysis. Unfortunately, many of them are too subjective to be measured and very …

Due to the growing privacy concerns, decentralization emerges rapidly in personalized
services, especially recommendation. Also, recent studies have shown that centralized …

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in
Location-based Social Networks (LBSNs) due to its effectiveness in hel** people decide …

Graph neural networks (GNNs) encounter significant computational challenges when
handling large-scale graphs, which severely restricts their efficacy across diverse …

Owing to the nature of privacy protection, feder-ated recommender systems (FedRecs) have
garnered increasing interest in the realm of on-device recommender systems. However …

As a step beyond traditional personalized recommendation, group recommendation is the
task of suggesting items that can satisfy a group of users. In group recommendation, the core …