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, big data, and network data analytics. I am a member of the IEEE, ACM and ACM SIGIR..

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Project

  • Graph representation learning
  • PyGCL: Graph Contrastive Learning Library for PyTorch

  • Information Verification
  • Research

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

    Graph Contrastive Learning with Adaptive Augmentation
    Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
    WWW, 2021, pdf
    Session-based Recommendation with Graph Neural Network
    Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
    AAAI, 2019, pdf
    A Convolutional Approach for Misinformation Identification
    Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
    IJCAI, 2017, pdf
    Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
    Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
    AAAI, 2016, pdf

    2023年
    Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning
    Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang
    WWW, 2023

    We propose a Hierarchical Relational Graph Neural Network to learn Long- and Short-term representations for TKG reasoning.


    2022年
    A Survey on Deep Graph Generation: Methods and Application
    Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu
    Learning on Graphs Conference (LOG), 2022, pdf

    We conduct a comprehensive review on the existing literature of deep graph generation from a variety of emerging methods to its wide application areas.

    Second-Order Global Attention Networks for Graph Classification and Regression
    Fenyu Hu, Zeyu Cui, Shu Wu, Qiang Liu, Jinlin Wu, Liang Wang, Tieniu Tan
    CICAI, 2022, pdf

    We propose a novel global attention module from two levels: channel level and node level. Specifically, we exploit second-order channel correlation to extract more discriminative representations.

    AI in Human-computer Gaming: Techniques, Challenges and Opportunities
    Qiyue Yin, Jun Yang, Kaiqi Huang, Meijing Zhao, Wancheng Ni, Bin Liang, Yan Huang, Shu Wu, Liang Wang
    Machine Intelligence Research, 2022, pdf

    We summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; raise the challenges or drawbacks of current techniques in the successful AIs; and try to point out future trends in human-computer gaming AIs.

    Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
    jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang
    TKDE, 2022, pdf

    We propose a latent structure MIning with ContRastive mOdality fusion method, which we term MICRO for brevity.

    MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning
    Yuwei Xia, Mengqi Zhang, Qiang Liu, Shu Wu, Xiao Yu Zhang
    EMNLP, 2022, pdf

    We propose a novel Temporal Meta-learning framework for temporal knowledge graph reasoning.

    GraphDIVE: Graph Classification by Mixture of Diverse Experts
    Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
    IJCAI, 2022, pdf

    To boost the performance of GNNs and investigate the relationship between topological structure and class imbalance, we propose GraphDIVE, which learns multi-view graph representations and combine multi-view experts.

    Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention
    Junfei Wu, Qiang Liu, Weizhi Xu, Shu Wu
    SIGIR, 2022, pdf

    We propose a novel framework for debiasing evidence-based fake news detection1 by causal intervention. Under this framework, the model is first trained on the original biased dataset like ordinary work.

    Deep Contrastive Multiview Network Embedding
    Mengqi Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang
    CIKM, 2022, pdf

    This work presents a novel Contrastive leaRning framEwork for Multiview network Embedding (CREME). In our work, different views can be obtained based on the various relations among nodes.

    The Devil is in the Conflict: Disentangled Information Graph Neural Networks For Fraud Detection
    Zhixun Li, Dingshuo Chen, Qiang Liu, Shu Wu
    ICDM, 2022, pdf

    We propose a simple and effective method that uses the attention mechanism to adaptively fuse two views which captures data-specific preference.

    A Unified Framework Based on Graph Consensus Term for Multiview Learning
    Xiangzhu Meng , Lin Feng , Chonghui Guo , Huibing Wang , Shu Wu
    TNNLS, 2022, pdf

    We aims at leveraging most existing graph embedding works into one formula via introducing the graph consensus term and proposes a unified and scalable multiview learning framework, termed graph consensus multiview framework (GCMF).

    RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
    Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin, Liang Wang
    TKDE, 2022, pdf

    We propose a novel Reject-aware Multi-Task Network (RMT-Net), which learns the task weights that control the information sharing from the rejection/approval task to the default/non-default task by a gating network based on rejection probabilities

    DyGCN: Efficient Dynamic Graph Embedding with Graph Convolutional Network
    Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Qiang Liu, Liang Wang, Mengmeng Ai
    TNNLS, 2022, pdf

    We propose an efficient dynamic graph embedding approach, Dynamic Graph Convolutional Network (DyGCN), which is an extension of GCN-based methods.

    Evidence-aware Fake News Detection with Graph Neural Networks
    Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, Liang Wang
    WWW, 2022, pdf

    We propose a unified Graph-based sEmantic sTructure mining framework, namely GET in short. Specifically, different from the existing work that treats claims and evidences as sequences, we model them as graph-structured data and capture the long-distance semantic dependency among dispersed relevant snippets via neighborhood propagation

    Structure-Enhanced Heterogeneous Graph Contrastive Learning
    Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu
    SDM, 2022, pdf

    We propose a novel multiview contrastiveaggregation objective to adaptively distill information from eachview. In addition, we advocate the explicit use of structure embed-ding, which enriches the model with local structural patterns of theunderlying HGs, so as to better mine true and hard negatives forGCL.

    Dynamic Graph Neural Networks for Sequential Recommendation
    Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang
    TKDE, 2022, pdf

    We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information,


    2021年
    An Empirical Study of Graph Contrastive Learning
    Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu
    NeurIPS, 2021

    We propose a general contrastive paradigm which characterizes previous work by limiting the design space of interest to four dimensions: (a) data augmentation functions, (b) contrasting modes, (c) contrastive objectives, and (d) negative mining strategies.

    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

    We propose a novel relevance matching model based on graph neural networks to leverage the documentlevel word relationships for ad-hoc retrieval.

    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.

    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.

    Graph Contrastive Learning with Adaptive Augmentation
    Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
    WWW, 2021, pdf

    We propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph.

    Relation-aware Heterogeneous Graph for User Profiling
    Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang Wang
    CIKM, 2021, pdf

    We propose to leverage the relation-aware heterogeneous graph method for user profiling, which also allows capturing significant meta relations.


    2020年
    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
    CIKM, 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.


    2019年
    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.

    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.

    Multi-view Clustering via Joint Feature Selection and Partially Constrained Cluster Label Learning
    Qiyue Yin, Junge Zhang, Shu Wu, Hexi Li
    PR, 2019, pdf

    We propose to optimize the cluster indicator, which representing the class labels is an intuitive reflection of the clustering structure.

    Semi-supervised Compatibility Learning across Categories for Clothing Matching
    Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, Liang Wang
    ICME, 2019, pdf

    We propose a semi-supervised method to learn the compatibility across categories.

    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

    We propose an Event2vec module to learn distributed representations of events in social media.

    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 Node-wise Graph Neural Networks (NGNN) which can better model node interactions and learn better node representations.

    Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction
    Qiang Cui, Yuyuan Tang, Shu Wu, Liang Wang
    PAKDD, 2019, pdf

    We propose to acquire the spatial preference by modeling distances between successive POIs.


    2018年
    Multi-view Clustering via Unified and View-Specific Embeddings Learning
    Qiyue Yin, Shu Wu, Liang Wang
    TNNLS, 2018, pdf

    This paper proposes to mimic different views as different relations in a knowledge graph for unified and view-specific embedding learning.

    Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach
    Qiang Liu, Feng Yu, Shu Wu, Liang Wang
    TIST, 2018, pdf

    We propose an attention-based approach for identification of misinformation (AIM).


    2017年
    A Convolutional Approach for Misinformation Identification
    Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
    IJCAI, 2017, pdf

    We propose a novel method, Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN).

    DeepStyle: Learning User Preferences for Visual Recommendation
    Qiang Liu, Shu Wu, Liang Wang
    SIGIR, 2017, pdf

    we propose a DeepStyle method for learning style features of items and sensing preferences of users.

    Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Mode
    Qiang Liu, Shu Wu, Liang Wang
    TKDE, 2017, pdf

    We propose a Recurrent Log-BiLinear (RLBL) model. It can model multiple types of behaviors in historical sequences with behavior-specific transition matrices.

    Unified Subspace Learning for Incomplete and Unlabeled Multi-view Data
    Qiyue Yin, Shu Wu, Liang Wang
    PR, 2017, pdf

    We propose a novel subspace learning framework for incomplete and unlabeled multi-view data.

    Blood Pressure Prediction via Recurrent Models with Contextual Layer
    Xiaohan Li, Shu Wu, Liang Wang
    WWW, 2017, pdf

    We propose a novel model named recurrent models with contextual layer, which can model the sequential measurement data and contextual data simultaneously to predict the trend of users’ BP.


    2016年
    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.

    Context-aware Sequential Recommendation
    Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, Liang Wang
    ICDM, 2016, pdf

    We propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN).

    Personalized Ranking with Pairwise Factorization Machines
    Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan
    Neurocomputing, 2016, pdf

    This work proposes a novel personalized ranking model which incorporates implicit feedback with content information by making use of Factorization Machines.

    A Dynamic Recurrent Model for Next Basket Recommendation
    Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
    SIGIR, 2016, pdf

    We propose a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN).

    Information Credibility Evaluation on Social Media
    Shu Wu, Qiang Liu, Yong Liu, Liang Wang, Tieniu Tan
    AAAI, Demo,2016, pdf

    We establish a Network Information Credibility Evaluation (NICE) platform, which collects a database of rumors that have been verified on Sina Weibo and automatically evaluates the information which is generated by users on social media but has not been verified.

    Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
    Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
    AAAI, 2016, pdf

    We extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN).


    2015年
    Incomplete Multi-view Clustering via Subspace Learning
    Qiyue Yin, Shu Wu, Liang Wang
    CIKM, 2015, pdf

    In this paper, a novel incomplete multi-view clustering method is therefore developed, which learns unified latent representations and projection matrices for the incomplete multi-view data.

    Collaborative Prediction for Multi-entity Interaction with Hierarchical Representation
    Qiang Liu, Shu Wu, Liang Wang
    CIKM, 2015, pdf

    We propose a Hierarchical Interaction Representation (HIR) model, which models the mutual action among different entities as a joint representation.

    Social-Relational Topic Model for Social Networks
    Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan
    CIKM, 2015, pdf

    We propose a novel Social-Relational Topic Model (SRTM), which can alleviate the effect of topic-irrelevant links by analyzing relational users’ topics of each link.

    A Convolutional Click Prediction Model
    Qiang Liu, Feng Yu, Shu Wu, Liang Wang
    CIKM, 2015, pdf

    We propose a novel model, Convolutional Click Prediction Model (CCPM), based on convolution neural network.

    Personalized Semantic Ranking for Collaborative Recommendation
    Song Xu, Shu Wu, Liang Wang
    SIGIR, 2015, pdf

    In this work, we present a unified framework, named Personalized Semantic Ranking (PSR).

    COT: Contextual Operating Tensor for Context-aware Recommender Systems
    Qiang Liu, Shu Wu, Liang Wang
    AAAI, Oral, 2015, pdf

    We propose Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector.


    2015年之前
    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.

    Rating-based Collaborative Filtering Combined with Additional Regularization
    Shu Wu and Shengrui Wang
    SIGIR, 2011, pdf

    We improve the conventional rating-based objective function by using ranking constraints as the supplementary regularization to restrict the searching of predicted ratings in smaller and more likely ranges, and develop a novel method, called RankSVD++, based on the SVD++ model.

    Academic Service
    • Senior Program Committee (SPC) member, The AAAI Conference on Artificial Intelligence (AAAI), 2018-2022.

    • Program Committee (TPC) member, The International Joint Conference on Artificial Intelligence (WWW), 2019-2022.

    • Program Committee (TPC) member, The Web Conference (WWW), 2019-2022.

    • Program Committee (TPC) member, The ACM International WSDM Conference (WSDM), 2019-2022.

    • 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).
    Research and Fundings
    • Co-principal InvestigatorKey technologies and applications for cross-scale systematic learning of social big data; 2022.01-2025.12; National Science Foundation of China (NSFC).

    • The Theory and Method for Detecting and Recognizing Fake Media Content in Social Network; 2020.01-2023.12; National Science Foundation of China (NSFC).

    • The Precision Service Technology of scientific and technological big data for the Individual Needs of Classified Users;2019.07-2022.06;National Key Research and Development Program of China.

    • Principal Investigator; Research on User Behavior Modeling Methods based on Fusion of Entity Feature and Sequential Information; 2018.01 - 2021.12; National Science Foundation of China (NSFC).

    • Principal Investigator; Social Recommendation with Contextual Information of Entity and Interaction; 2015.01 - 2017.12; National Science Foundation of China (NSFC).

    • Principal Investigator; Research on Modeling User Behavior Methods based on Fusion of Entity Feature and Sequence Analysis;2018.01 - 2020.12; Beijing Natural Science Foundation (BJNSF).

    • Principal Investigator; CASIA-JD Finance, Intelligence Financial Risk Joint Laboratory.

    • Principal Investigator; Research on recommendation algorithms for mobile games; 2017.05 - 2017.10; Tencent.

    • Principal Investigator; Abnormal behavior detection based on deep neural network and sparse coding; 2016.09 - 2017.10; CCF-Venustech Hongyan Research Fund.

    • Principal Investigator; Click through rate prediction based on recurrent neural network; 2016.08 - 2017.10; CCF-Tencent Open Fund.

    • Principal Investigator; Collaborative Prediction of High Blood Pressure with Contextual Information; 2016.01 - 2016.06; iHealth and Andon.

    • Principal Investigator; Public Opinion Monitoring System based on Multi-modal Data; 2015.01 - 2017.12; MSR-CNIC Windows Azure Project.

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