Recommender systems aim to provide item recommendations for users and are usually
faced with data sparsity problems (eg, cold start) in real-world scenarios. Recently pre …

Traffic flow forecasting has always been a challenge owing to its complicated spatiotemporal
dependencies. Few of previous works can exploit the implicit interactions among traffic …

The traditional rumor diffusion model primarily studies the rumor itself and user behavior as
the entry points. The complexity of user behavior, multidimensionality of the communication …

This study is inspired by the current image restoration technology. If we regard the users
participating in the rumor as image pixels, similar to social networks, the recovery of pixel …

Link prediction plays an important role in network analysis and applications. Recently,
approaches for link prediction have evolved from traditional similarity-based algorithms into …

In social topics, the study of rumor propagation mechanisms is essential to master social
behavior. In this paper, aiming to explore the adversarial game relationship between rumor …

Existing sequential recommendation methods rely on large amounts of training data and
usually suffer from the data sparsity problem. To tackle this, the pre-training mechanism has …

Fine-grained crowd distribution forecasting benefits smart transportation operations and
management, such as public transport dispatch, traffic demand prediction, and transport …

In designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

In the last decade, crowdfunding has emerged as a new form of Internet finance, providing
founders with a channel through which they can raise funds from the public. Prior studies …