Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …

Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …

Network quantization has gained increasing attention with the rapid growth of large pre-
trained language models~(PLMs). However, most existing quantization methods for PLMs …

Graph representation learning via Contrastive Learning (GCL) has drawn considerable
attention recently. Efforts are mainly focused on gathering more global information via …

Personalized recommender systems have found widespread applications for effective
information filtering. Conventional models engage in knowledge mining within the static …

Searching on bipartite graphs is basal and versatile to many real-world Web applications,
eg, online recommendation, database retrieval, and query-document searching. Given a …

The classic problem of node importance estimation has been conventionally studied with
homogeneous network topology analysis. To deal with practical network heterogeneity, a …

Maximizing the user-item engagement based on vectorized embeddings is a standard
procedure of recent recommender models. Despite the superior performance for item …

Discrimination against minority groups within the banking sector has long resulted in
unequal treatment in financial services. Recent works in the general machine learning …

The prevalence of tree-like structures, encompassing hierarchical structures and power law
distributions, exists extensively in real-world applications, including recommendation …