b'Shu Wu'

research

∙ 03/23/2023

Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation

In recommendation systems, a large portion of the ratings are missing du...

0 Wenbo Hu, et al. ∙

research

∙ 02/02/2023

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning

Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future f...

0 Yuwei Xia, et al. ∙

research

∙ 10/22/2022

The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection

Graph-based fraud detection has heretofore received considerable attenti...

0 Zhixun Li, et al. ∙

research

∙ 09/29/2022

Improving Molecular Pretraining with Complementary Featurizations

Molecular pretraining, which learns molecular representations over massi...

6 Yanqiao Zhu, et al. ∙

research

∙ 03/13/2022

A Survey on Deep Graph Generation: Methods and Applications

Graphs are ubiquitous in encoding relational information of real-world o...

47 Yanqiao Zhu, et al. ∙

research

∙ 10/14/2021

Relation-aware Heterogeneous Graph for User Profiling

User profiling has long been an important problem that investigates user...

15 Qilong Yan, et al. ∙

research

∙ 09/02/2021

An Empirical Study of Graph Contrastive Learning

Graph Contrastive Learning (GCL) establishes a new paradigm for learning...

13 Yanqiao Zhu, et al. ∙

research

∙ 08/31/2021

Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de fa...

5 Yanqiao Zhu, et al. ∙

research

∙ 08/16/2021

Deep Contrastive Learning for Multi-View Network Embedding

Multi-view network embedding aims at projecting nodes in the network to ...

13 Mengqi Zhang, et al. ∙

research

∙ 08/15/2021

Deep Active Learning for Text Classification with Diverse Interpretations

Recently, Deep Neural Networks (DNNs) have made remarkable progress for ...

0 Qiang Liu, et al. ∙

research

∙ 08/10/2021

Fully Hyperbolic Graph Convolution Network for Recommendation

Recently, Graph Convolution Network (GCN) based methods have achieved ou...

0 Liping Wang, et al. ∙

research

∙ 08/10/2021

Label-informed Graph Structure Learning for Node Classification

Graph Neural Networks (GNNs) have achieved great success among various d...

0 Liping Wang, et al. ∙

research

∙ 05/25/2021

GraphFM: Graph Factorization Machines for Feature Interaction Modeling

Factorization machine (FM) is a prevalent approach to modeling pairwise ...

7 Zekun Li, et al. ∙

research

∙ 05/22/2021

Explainable Enterprise Credit Rating via Deep Feature Crossing Network

Due to the powerful learning ability on high-rank and non-linear feature...

0 Weiyu Guo, et al. ∙

research

∙ 04/26/2021

Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation

Item-based collaborative filtering (ICF) has been widely used in industr...

0 Yinjiang Cai, et al. ∙

research

∙ 04/19/2021

Mining Latent Structures for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Investigati...

9 Jinghao Zhang, et al. ∙

research

∙ 04/15/2021

Dynamic Graph Neural Networks for Sequential Recommendation

Modeling users' preference from his historical sequences is one of the c...

0 Mengqi Zhang, et al. ∙

research

∙ 04/07/2021

DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

Graph embedding, aiming to learn low-dimensional representations (aka. e...

43 Zeyu Cui, et al. ∙

research

∙ 03/29/2021

Graph Classification by Mixture of Diverse Experts

Graph classification is a challenging research problem in many applicati...

0 Fenyu Hu, et al. ∙

research

∙ 03/04/2021

Deep Graph Structure Learning for Robust Representations: A Survey

Graph Neural Networks (GNNs) are widely used for analyzing graph-structu...

18 Yanqiao Zhu, et al. ∙

research

∙ 02/22/2021

Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval

The ad-hoc retrieval task is to rank related documents given a query and...

0 Xueli Yu, et al. ∙

research

∙ 01/28/2021

A Graph-based Relevance Matching Model for Ad-hoc Retrieval

To retrieve more relevant, appropriate and useful documents given a quer...

0 Yufeng Zhang, et al. ∙

research

∙ 01/11/2021

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

Click-through rate (CTR) prediction, which aims to predict the probabili...

0 Yanqiao Zhu, et al. ∙

research

∙ 01/08/2021

Dynamic Graph Collaborative Filtering

Dynamic recommendation is essential for modern recommender systems to pr...

29 Xiaohan Li, et al. ∙

research

∙ 12/10/2020

Cold-start Sequential Recommendation via Meta Learner

This paper explores meta-learning in sequential recommendation to allevi...

0 Yujia Zheng, et al. ∙

research

∙ 11/13/2020

Heterogeneous Graph Collaborative Filtering

Graph-based collaborative filtering (CF) algorithms have gained increasi...

5 Zekun Li, et al. ∙

research

∙ 10/30/2020

Deep Active Graph Representation Learning

Graph neural networks (GNNs) aim to learn graph representations that pre...

28 Yanqiao Zhu, et al. ∙

research

∙ 10/27/2020

Graph Contrastive Learning with Adaptive Augmentation

Recently, contrastive learning (CL) has emerged as a successful method f...

10 Yanqiao Zhu, et al. ∙

research

∙ 09/21/2020

DGTN: Dual-channel Graph Transition Network for Session-based Recommendation

The task of session-based recommendation is to predict user actions base...

0 Yujia Zheng, et al. ∙

research

∙ 09/03/2020

CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning

Unsupervised graph representation learning aims to learn low-dimensional...

7 Yanqiao Zhu, et al. ∙

research

∙ 08/27/2020

One Shot 3D Photography

3D photography is a new medium that allows viewers to more fully experie...

10 Johannes Kopf, et al. ∙

research

∙ 08/17/2020

Disentangled Item Representation for Recommender Systems

Item representations in recommendation systems are expected to reveal th...

0 Zeyu Cui, et al. ∙

research

∙ 06/29/2020

TFNet: Multi-Semantic Feature Interaction for CTR Prediction

The CTR (Click-Through Rate) prediction plays a central role in the doma...

0 Shu Wu, et al. ∙

research

∙ 06/07/2020

Deep Graph Contrastive Representation Learning

Graph representation learning nowadays becomes fundamental in analyzing ...

10 Yanqiao Zhu, et al. ∙

research

∙ 05/06/2020

TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

Session-based recommendation nowadays plays a vital role in many website...

0 Feng Yu, et al. ∙

research

∙ 04/22/2020

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

Text classification is fundamental in natural language processing (NLP),...

0 Yufeng Zhang, et al. ∙

research

∙ 11/26/2019

Independence Promoted Graph Disentangled Networks

We address the problem of disentangled representation learning with inde...

0 Yanbei Liu, et al. ∙

research

∙ 11/11/2019

Learning Preferences and Demands in Visual Recommendation

Visual information is an important factor in recommender systems, in whi...

0 Qiang Liu, et al. ∙

research

∙ 11/05/2019

GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction

Graph representation learning is of paramount importance for a variety o...

15 Fenyu Hu, et al. ∙

research

∙ 10/20/2019

Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recommendation

The problem of personalized session-based recommendation aims to predict...

0 Shu Wu, et al. ∙

research

∙ 10/12/2019

Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

Click-through rate (CTR) prediction is an essential task in web applicat...

0 Zekun Li, et al. ∙

research

∙ 07/31/2019

Semi-supervised Compatibility Learning Across Categories for Clothing Matching

Learning the compatibility between fashion items across categories is a ...

0 Zekun Li, et al. ∙

research

∙ 02/26/2019

Learning Vertex Convolutional Networks for Graph Classification

In this paper, we develop a new aligned vertex convolutional network mod...

0 Lu Bai, et al. ∙

research

∙ 02/21/2019

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

With the rapid development of fashion market, the customers' demands of ...

0 Zeyu Cui, et al. ∙

research

∙ 02/13/2019

Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks

Graph convolutional networks (GCNs) have been successfully applied in no...

8 Fenyu Hu, et al. ∙

research

∙ 11/01/2018

Session-based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict users' actio...

0 Shu Wu, et al. ∙

research

∙ 11/14/2017

A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation

Sequential recommendation is one of fundamental tasks for Web applicatio...

0 Qiang Cui, et al. ∙

research

∙ 09/29/2016

ICE: Information Credibility Evaluation on Social Media via Representation Learning

With the rapid growth of social media, rumors are also spreading widely ...

0 Qiang Liu, et al. ∙

research

∙ 09/19/2016

Context-aware Sequential Recommendation

Since sequential information plays an important role in modeling user be...

0 Qiang Liu, et al. ∙

research

∙ 07/21/2016

A Comprehensive Survey on Cross-modal Retrieval

In recent years, cross-modal retrieval has drawn much attention due to t...

0 Kaiye Wang, et al. ∙

Shu Wu

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