Chen Gao

Recommender Systems with Characterized Social Regularization

2018

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social network. Existing social recommendation methods are based on the fact that users …

Mixed Attention Network for Cross-domain Sequential Recommendation

2023 · arXiv (Cornell University)

In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which …

Dual contrastive enhancement-based multimodal recommendation

2025

Multi-modal recommendation systems are widely applied in user service fields such as e-commerce and social platforms. They have become a key method to improve user experience and promote personalized recommendations. However, existing recommendation systems face …

Multi-behavior Recommendation with Graph Convolutional Networks

2020

Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks …

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

2021

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users’ conformity towards popular items, which entangles users’ real interest. Existing methods tracks this problem as eliminating popularity bias, …

Sequential Recommendation with Graph Neural Networks

2021

Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation. First, user behaviors in their rich historical sequences are often …

Graph Neural Networks for Recommender System

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse …

Disentangling Long and Short-Term Interests for Recommendation

2022 · Proceedings of the ACM Web Conference 2022

Modeling user’s long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may …

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

2023 · ACM Transactions on Recommender Systems

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 recommender systems. In this survey, we conduct a comprehensive review of …