Multi-task learning has been widely applied in computational vision, natural language
processing and other fields, which has achieved well performance. In recent years, a lot of …

Multi-domain recommendation (MDR) aims to improve the recommendation performance in
all target domains simultaneously by leveraging rich data from relevant domains. However …

Recent advancements in recommender systems have focused on integrating knowledge
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …

Recently, there has been a surge in recommendations based on heterogeneous information
networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite …

Multi-scenario and multi-task recommendation can use various feedback behaviors of users
in different scenarios to learn users' preferences and then make recommendations, which …

Accurately predicting the highway traffic speed can reduce traffic accidents and transit time,
and it also provides valuable reference data for traffic control in advance. Three essential …

Understanding human mobility in urban areas is important for transportation, from planning
to operations and online control. This paper proposes the concept of user-station attention …

While Knowledge Graphs (KGs) have long been used as valuable sources of structured
knowledge, in recent years, KG embeddings have become a popular way of deriving …

In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent
subtasks carry a wealth of shared knowledge that can be utilized to enhance the …

In the realm of modern recommender systems, user-item interaction data often exhibit
sequential patterns in relation to various behaviors, such as clicks and purchases on e …