Yankai Chen

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

2022 · arXiv (Cornell University)

Learning vectorized embeddings is at the core of various recommender systems for user-item matching. To perform efficient online inference, representation quantization, aiming to embed the latent features by a compact sequence of discrete numbers, recently …

Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation

2024

Understanding customer behavior is crucial for improving service quality in large-scale E-commerce. This paper proposes C-STAR, a new framework that learns compact representations from customer shopping journeys, with good versatility to fuel multiple downstream customer-centric …

Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularization

2025

Node importance estimation, a classical problem in network analysis, underpins various web applications. Previous methods either exploit intrinsic topological characteristics, e.g., graph centrality, or leverage additional information, e.g., data heterogeneity, for node feature enhancement. However, …