b'Shu Wu'

GraphFM: Graph Factorization Machines for Feature Interaction Modeling

Factorization machine (FM) is a prevalent approach to modeling pairwise ...
05/25/2021 ∙ by Zekun Li, et al. ∙ 7 ∙ share

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Explainable Enterprise Credit Rating via Deep Feature Crossing Network

Due to the powerful learning ability on high-rank and non-linear feature...
05/22/2021 ∙ by Weiyu Guo, et al. ∙ 0 ∙ share

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

Item-based collaborative filtering (ICF) has been widely used in industr...
04/26/2021 ∙ by Yinjiang Cai, et al. ∙ 0 ∙ share

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Mining Latent Structures for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Investigati...
04/19/2021 ∙ by Jinghao Zhang, et al. ∙ 8 ∙ share

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Dynamic Graph Neural Networks for Sequential Recommendation

Modeling users' preference from his historical sequences is one of the c...
04/15/2021 ∙ by Mengqi Zhang, et al. ∙ 0 ∙ share

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DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

Graph embedding, aiming to learn low-dimensional representations (aka. e...
04/07/2021 ∙ by Zeyu Cui, et al. ∙ 43 ∙ share

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Graph Classification by Mixture of Diverse Experts

Graph classification is a challenging research problem in many applicati...
03/29/2021 ∙ by Fenyu Hu, et al. ∙ 0 ∙ share

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Deep Graph Structure Learning for Robust Representations: A Survey

Graph Neural Networks (GNNs) are widely used for analyzing graph-structu...
03/04/2021 ∙ by Yanqiao Zhu, et al. ∙ 18 ∙ share

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

The ad-hoc retrieval task is to rank related documents given a query and...
02/22/2021 ∙ by Xueli Yu, et al. ∙ 0 ∙ share

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

To retrieve more relevant, appropriate and useful documents given a quer...
01/28/2021 ∙ by Yufeng Zhang, et al. ∙ 0 ∙ share

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

Click-through rate (CTR) prediction, which aims to predict the probabili...
01/11/2021 ∙ by Yanqiao Zhu, et al. ∙ 0 ∙ share

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Dynamic Graph Collaborative Filtering

Dynamic recommendation is essential for modern recommender systems to pr...
01/08/2021 ∙ by Xiaohan Li, et al. ∙ 29 ∙ share

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Cold-start Sequential Recommendation via Meta Learner

This paper explores meta-learning in sequential recommendation to allevi...
12/10/2020 ∙ by Yujia Zheng, et al. ∙ 0 ∙ share

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Heterogeneous Graph Collaborative Filtering

Graph-based collaborative filtering (CF) algorithms have gained increasi...
11/13/2020 ∙ by Zekun Li, et al. ∙ 5 ∙ share

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Deep Active Graph Representation Learning

Graph neural networks (GNNs) aim to learn graph representations that pre...
10/30/2020 ∙ by Yanqiao Zhu, et al. ∙ 28 ∙ share

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Graph Contrastive Learning with Adaptive Augmentation

Recently, contrastive learning (CL) has emerged as a successful method f...
10/27/2020 ∙ by Yanqiao Zhu, et al. ∙ 10 ∙ share

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

The task of session-based recommendation is to predict user actions base...
09/21/2020 ∙ by Yujia Zheng, et al. ∙ 0 ∙ share

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

Unsupervised graph representation learning aims to learn low-dimensional...
09/03/2020 ∙ by Yanqiao Zhu, et al. ∙ 7 ∙ share

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One Shot 3D Photography

3D photography is a new medium that allows viewers to more fully experie...
08/27/2020 ∙ by Johannes Kopf, et al. ∙ 10 ∙ share

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Disentangled Item Representation for Recommender Systems

Item representations in recommendation systems are expected to reveal th...
08/17/2020 ∙ by Zeyu Cui, et al. ∙ 0 ∙ share

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TFNet: Multi-Semantic Feature Interaction for CTR Prediction

The CTR (Click-Through Rate) prediction plays a central role in the doma...
06/29/2020 ∙ by Shu Wu, et al. ∙ 0 ∙ share

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Deep Graph Contrastive Representation Learning

Graph representation learning nowadays becomes fundamental in analyzing ...
06/07/2020 ∙ by Yanqiao Zhu, et al. ∙ 10 ∙ share

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

Session-based recommendation nowadays plays a vital role in many website...
05/06/2020 ∙ by Feng Yu, et al. ∙ 0 ∙ share

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

Text classification is fundamental in natural language processing (NLP),...
04/22/2020 ∙ by Yufeng Zhang, et al. ∙ 0 ∙ share

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Independence Promoted Graph Disentangled Networks

We address the problem of disentangled representation learning with inde...
11/26/2019 ∙ by Yanbei Liu, et al. ∙ 0 ∙ share

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Learning Preferences and Demands in Visual Recommendation

Visual information is an important factor in recommender systems, in whi...
11/11/2019 ∙ by Qiang Liu, et al. ∙ 0 ∙ share

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GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction

Graph representation learning is of paramount importance for a variety o...
11/05/2019 ∙ by Fenyu Hu, et al. ∙ 15 ∙ share

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

The problem of personalized session-based recommendation aims to predict...
10/20/2019 ∙ by Shu Wu, et al. ∙ 0 ∙ share

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

Click-through rate (CTR) prediction is an essential task in web applicat...
10/12/2019 ∙ by Zekun Li, et al. ∙ 0 ∙ share

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Semi-supervised Compatibility Learning Across Categories for Clothing Matching

Learning the compatibility between fashion items across categories is a ...
07/31/2019 ∙ by Zekun Li, et al. ∙ 0 ∙ share

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Learning Vertex Convolutional Networks for Graph Classification

In this paper, we develop a new aligned vertex convolutional network mod...
02/26/2019 ∙ by Lu Bai, et al. ∙ 0 ∙ share

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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 ...
02/21/2019 ∙ by Zeyu Cui, et al. ∙ 0 ∙ share

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Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks

Graph convolutional networks (GCNs) have been successfully applied in no...
02/13/2019 ∙ by Fenyu Hu, et al. ∙ 8 ∙ share

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Session-based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict users' actio...
11/01/2018 ∙ by Shu Wu, et al. ∙ 0 ∙ share

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A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation

Sequential recommendation is one of fundamental tasks for Web applicatio...
11/14/2017 ∙ by Qiang Cui, et al. ∙ 0 ∙ share

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ICE: Information Credibility Evaluation on Social Media via Representation Learning

With the rapid growth of social media, rumors are also spreading widely ...
09/29/2016 ∙ by Qiang Liu, et al. ∙ 0 ∙ share

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Context-aware Sequential Recommendation

Since sequential information plays an important role in modeling user be...
09/19/2016 ∙ by Qiang Liu, et al. ∙ 0 ∙ share

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A Comprehensive Survey on Cross-modal Retrieval

In recent years, cross-modal retrieval has drawn much attention due to t...
07/21/2016 ∙ by Kaiye Wang, et al. ∙ 0 ∙ share

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Shu Wu

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