To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for
downstream tasks with unlabeled data, and it has recently become an active research area …

The recommender system (RS) has been an integral toolkit of online services. They are
equipped with various deep learning techniques to model user preference based on …

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

Cross-domain recommendation can help alleviate the data sparsity issue in traditional
sequential recommender systems. In this paper, we propose the RecGURU algorithm …

Configuration knobs of database systems are essential to achieve high throughput and low
latency. Recently, automatic tuning systems using machine learning methods (ML) have …

We study the problem of making item recommendations to ephemeral groups, which
comprise users with limited or no historical activities together. Existing studies target …

We study the problem of recommending relevant products to users in relatively resource-
scarce markets by leveraging data from similar, richer in resource auxiliary markets. We …