Bolin Ding

Scalable Graph Neural Networks via Bidirectional Propagation

2020 · arXiv (Cornell University)

Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or "layer-wise …

Deep Efficient Private Neighbor Generation for Subgraph Federated Learning

2024 · Society for Industrial and Applied Mathematics eBooks

Behemoth graphs are often fragmented and separately stored by multiple data owners as distributed subgraphs in many realistic applications. Without harming data privacy, it is natural to consider the subgraph federated learning (subgraph FL) scenario, …

Contrastive Learning for Sequential Recommendation

2022 · 2022 IEEE 38th International Conference on Data Engineering (ICDE)

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical inter-actions. Despite their success, we argue that these approaches usually rely …

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

2021

Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversations. Reinforcement learning (RL) is widely adopted to learn conversational recommendation policies to decide what …

Towards Universal Sequence Representation Learning for Recommender Systems

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL methods rely on explicit item IDs for developing the sequence …