Wu Shu(吴 书)

I am currently an Associated Professor in the Center for Research on Intelligent Perception and Computing (CRIPAC) , National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences.

My research interests include data mining, recommendation systems, pervasive computing, and network data analytics. I am a member of the IEEE, ACM and ACM SIGIR..

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Research

I'm interested in data mining, recommendation systems, pervasive computing. Representative papers are highlighted.

Session-based Recommendation with Graph Neural Network
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
AAAI, 2019, pdf

we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.

Contextual Operation for Recommender Systems
Shu Wu, Qiang Liu, Liang Wang, Tieniu Tan
TKDE, 2016, pdf

we represent each context value with a latent vector, and model the contextual information as a semantic operation on the user and item.

Coupled Topic Model for Collaborative Filtering with User-Generated Content
Shu Wu, Weiyu Guo, Song Xu, Yongzhen Huang, Liang Wang
THMS, 2016, pdf

In this study, a coupled topic model (CoTM) for recommendation with UGC is developed.

Information-theoretic Outlier Detection for large-scale Categorical Data
Shu Wu, Shengrui Wang
TKDE, 2013, pdf

We propose two practical 1-parameter outlier detection methods, named ITB-SS and ITB-SP, which require no user-defined parameters for deciding whether an object is an outlier.

Personalized graph neural networks with attention mechanism for session-aware recommendation
Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, Liang Wang
TKDE, 2021, pdf

we propose a novel method, named Personalized Graph Neural Networks with Attention Mechanism (A-PGNN) for brevity.

A Graph-based Relevance Matching Model for Ad-hoc Retrieval
Yufeng Zhang, Jinghao Zhang, Zeyu Cui, Shu Wu, Liang Wang
AAAI, 2021, pdf

Our approach significantly outperforms strong baselines on two ad-hoc benchmarks. We also experimentally compare our model with BERT and show our advantages on long documents.

Cold-start Sequential Recommendation via Meta Learner
Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu
AAAI, 2021, pdf

we propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation.

Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
Xueli Yu,Weizhi Xu,Zeyu Cui,Shu Wu,Liang Wang
WWW, 2021, pdf

we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hierarchical matching signals simultaneously.

Disentangled Item Representation for Recommender Systems
Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, Liang Wang
ACM TIST, 2021, pdf

we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this paper, where the items are represented as several separated attribute vectors instead of a single latent vector.

GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan
PR, 2021, pdf

we first theoretically prove that the effect of non-linear activation functions in GCNs is to introduce the interaction terms of neighborhood features. We then show that coefficients of the neighborhood interacting terms are relatively small in current GCN-based models. To this end, we present a general framework named GraphAIR.

Mining Latent Structures for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang
MM, 2021, pdf

we propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity.

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu, Shu Wu
CIKM, 2021, pdf

we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.

Deep Active Learning for Text Classification with Diverse Interpretations
Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu
CIKM, 2021, pdf

we propose a novel Active Learning with DivErse iNterpretations (ALDEN) approach.

Label-informed Graph Structure Learning for Node Classification
Liping Wang, Fenyu Hu, Shu Wu, Liang Wang
CIKM, 2021, pdf

we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix.

Fully Hyperbolic Graph Convolution Network for Recommendation
Liping Wang, Fenyu Hu, Shu Wu, Liang Wang
CIKM, 2021, pdf

we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space.

Motif-aware Sequential Recommendation
Zeyu Cui, Yinjiang Cai, Shu Wu, Xibo Ma, Liang Wang
SIGIR, 2021, pdf

we propose a novel model called Motifaware Sequential Recommendation (MoSeR), which captures the motifs hidden in behavior sequences to model the micro-structure features.

Independence Promoted Graph Disentangled Networks
Yanbei Liu, Xiao Wang, Shu Wu, Zhitao Xiao
AAAI, 2020, pdf

we propose a novel Independence Promoted Graph Disentangled Networks (IPGDN) to learn disentangled node representation while enhancing the independence among node representations.

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, Liang Wang
ACL, 2020, pdf

we propose TextING for inductive text classification via GNN.

TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
SIGIR, 2020, pdf

we propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation.

TFNet: Multi-Semantic Feature Interaction for CTR Prediction
Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao, Fan Huang
SIGIR, 2020, pdf

we propose a novel Tensor-based Feature interaction Network (TFNet) model, which introduces an operating tensor to elaborate feature interactions via multi-slice matrices in multiple semantic spaces.

Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, Liang Wang
SIGIR, 2020, pdf

we propose a novel Interaction Machine (IM) model. IM is an ecient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature elds.

Deep Graph Contrastive Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
GRL+@ICML, 2020, pdf

we propose a hybrid scheme for generating graph views on both structure and attribute levels.

Dynamic Graph Collaborative Filtering
Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang
ICDM, 2020, pdf

we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.

GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan,
PR, 2020, pdf

we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation.

Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, Liang Wang
CIKM, 2019, pdf

we propose to represent the multi-field features in a graph structure intuitively, where each node corresponds to a feature field and different fields can interact through edges

Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor
Jingyi Wang, Qiang Liu, Zhaocheng Liu, Shu Wu
CIKM, 2019, pdf

we propose a CNN & Attention-based Sequential Feature Extractor (CASFE) module to capture the possible features of user behaviors at different time intervals.

Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan
IJCAI, 2019, pdf

we propose a novel deep Hierarchical Graph Convolutional Network (H-GCN) for semisupervised node classification.

A Hierarchical Contextual Attention-based Network for Sequential Recommendation
Qiang Cui, Shu Wu, Yan Huang, Liang Wang
Neurocomputing, 2019, pdf

we propose a Hierarchical Contextual Attention-based GRU (HCA-GRU) network.

Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts
Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
Computers & Security, 2019, pdf

It is necessary to give a comprehensive review of the new research trends of FID..

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Liang Wang
WWW, 2019, pdf

we propose to represent an outfit as a graph.

MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong, Liang Wang,
TKDE, 2019, pdf

we propose a Multi-View Rrecurrent Neural Network (MV-RNN) mode.

Service
Technical Program Committee (TPC) member, The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD), 2018.

Senior Member, China Computer Federation (CCF), 2017-.

Associate Editor, Frontiers of Computer Science, 2016-

Co-Chair, Multimedia and Social Networking, The 28th IEEE International Conference on Advanced Information Networking and Applications (AINA-2014)

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