112 citations · 172 across the 6 of their papers we have counts for
9 papers
Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation
Yinjiang Cai, Zeyu Cui, Shu Wu +2
Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising. It models users' preference on target it…
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Zeyu Cui, Zekun Li, Shu Wu +4
Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of eff…
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
Xueli Yu, Weizhi Xu, Zeyu Cui +2
The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem…
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
Yufeng Zhang, Jinghao Zhang, Zeyu Cui +2
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task a…
Disentangled Item Representation for Recommender Systems
Zeyu Cui, Feng Yu, Shu Wu +2
Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vect…
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
Yufeng Zhang, Xueli Yu, Zeyu Cui +3
Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works…