1.5k citations · 4.9k across the 104 of their papers we have counts for
11 papers · 1 filter
Cold-start Sequential Recommendation via Meta Learner
Yujia Zheng, Siyi Liu, Zekun Li +1
This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based…
Heterogeneous Graph Collaborative Filtering
Zekun Li, Yujia Zheng, Shu Wu +2
Graph-based collaborative filtering (CF) algorithms have gained increasing attention. Existing work in this literature usually models the user-item interactions as a bipartite grap…
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
Yanqiao Zhu, Weizhi Xu, Qiang Liu +1
This paper studies active learning (AL) on graphs, whose purpose is to discover the most informative nodes to maximize the performance of graph neural networks (GNNs). Previously,…
Graph Contrastive Learning with Adaptive Augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu +3
Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation o…
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
Yujia Zheng, Siyi Liu, Zekun Li +1
The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to captur…
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu +2
Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive feat…
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…
Deep Graph Contrastive Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu +3
Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel…
TFNet: Multi-Semantic Feature Interaction for CTR Prediction
Shu Wu, Feng Yu, Xueli Yu +5
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems. There exists several kinds of methods proposed in t…
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…
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Feng Yu, Yanqiao Zhu, Qiang Liu +3
Session-based recommendation nowadays plays a vital role in many websites, which aims to predict users' actions based on anonymous sessions. There have emerged many studies that mo…