Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Knowledge graphs (KGs) could effectively integrate a large number of real-world assertions,
and improve the performance of various applications, such as recommendation and search …

Graph neural networks (GNNs) have received remarkable success in link prediction
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …

Optimization problems with nonlinear cost functions and combinatorial constraints appear in
many real-world applications but remain challenging to solve efficiently compared to their …

Training graph neural networks (GNNs) is extremely time consuming because sparse graph-
based operations are hard to be accelerated by community hardware. Prior art successfully …

Deep Learning Recommendation Models (DLRMs) are very popular in personalized
recommendation systems and are a major contributor to the data-center AI cycles. Due to the …

Graph neural networks (GNNs) have shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …

Deep learning recommendation systems serve personalized content under diverse tail-
latency targets and input-query loads. In order to do so, state-of-the-art recommendation …

The deployment of Deep Learning Recommendation Models (DLRMs) involves the
parallelization of extra-large embedding tables (EMTs) on multiple GPUs. Existing works …

Recommendation models rely on deep learning networks and large embedding tables,
resulting in computationally and memory-intensive processes. These models are typically …