In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Sequential recommendation aims to leverage users' historical behaviors to predict their next
interaction. Existing works have not yet addressed two main challenges in sequential …

Industrial recommender systems have been growing increasingly complex, may
involve\emph {diverse domains} such as e-commerce products and user-generated …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recommender systems play a vital role in various online services. However, the insulated
nature of training and deploying separately within a specific domain limits their access to …

Cold-start problem is still a very challenging problem in recommender systems. Fortunately,
the interactions of the cold-start users in the auxiliary source domain can help cold-start …

Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …