Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …

Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

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

Deep learning recommendation models (DLRMs) are using increasingly larger embedding
tables to represent categorical sparse features such as video genres. Each embedding row …

The recommender system which gets higher in practical use in applying the Apriori
algorithm in the early 2000s has revolutionized our daily life as it currently is widely used by …

Ensuring high-quality recommendations for newly onboarded users requires the continuous
retraining of Deep Learning Recommendation Models (DLRMs) with freshly generated data …

Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising
results. However, the challenges of deploying LLM4Rec in real-world scenarios remain …

Recently, graph neural network (GNN) approaches have received huge interests in
recommendation tasks due to their ability of learning more effective user and item …

Bulk data replication across multiple cloud regions and providers is essential for large
organizations to support data analytics, disaster recovery, and geo-distributed model …

Embedding columns are important for deep recommendation models to achieve high
accuracy, but they can be very time-consuming during inference. Machine learning (ML) …