Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

Recent years have witnessed the great accuracy performance of graph-based Collaborative
Filtering (CF) models for recommender systems. By taking the user-item interaction behavior …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Recent years are witnessing the rapid development of social recommendation to improve
the performance of recommender system, especially for cold start problem. Many of the …

Multi-behavior recommendation exploits multiple types of user-item interactions, such as
view and cart, to learn user preferences and has demonstrated to be an effective solution to …

Financial services are increasingly personalized due to the rapid development of artificial
intelligence. In particular, precision marketing is the ultimate goal in providing financial …

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS)
due to their superiority in modeling collaborative patterns. Although improving the overall …

Graph neural networks have been frequently applied in recommender systems due to their
powerful representation abilities for irregular data. However, these methods still suffer from …

Knowledge tracing aims to trace students' evolving knowledge states by predicting their
future performance on concept-related exercises. Recently, some graph-based models have …

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-
linguistic input data, such as tables and attribute–value pairs, but overlook that different …