Yingxue Zhang

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks

2020

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize …

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 …

Plain Transformers Can be Powerful Graph Learners

2025 · arXiv (Cornell University)

Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms. Researchers have attempted to migrate Transformers to graph learning, but most advanced Graph Transformers (GTs) have strayed …