Research Focus
  • Next Generation Multi-scenario and Multi-modal Heterogeneous Computing Engines

Our research focuses on fusing and unifying various computing paradigms such as batch processing, streaming, and interactive analysis, by developing new technologies such as approximate and progressive query processing. This will be utilized to empower next-generation computing engines used by real-world applications at Alibaba, and to facilitate efficient execution of hybrid workloads including traditional data analytics, graph computing, and machine learning.

Meanwhile, the lab studies how to leverage and integrate high-performance heterogeneous hardware, such as GPU, FPGA, and ASIC, with general-purpose computing engines to enhance their power. This will be utilized to better meet the requirements of data-intensive computation such as artificial intelligence and large-scale data analytics.

  • Algorithms and Applications of Large-scale Diverse Data Mining and Machine Learning

Our research focuses on efficient data mining and machine learning algorithms on large-scale heterogeneous data, such as structured data, graph data, and information networks. We also explore and fuse new techniques in the fields of large-scale graph representation learning and knowledge base, and applies them to scenarios such as online fraud prevention, recommendation systems, and search.

  • Smart and Autonomous System

Our research focuses on powering data management and processing systems with artificial intelligence technology: artificial-intelligence techniques will be used in data-warehouse management, resource scheduling, and engine optimization to strengthen the systems and make them smarter, safer, and more reliable. Moreover, system technology will be used to assist model selection and hyperparameter search in artificial intelligence and automate machine learning.

  • Data Security and Privacy Protection

Our research focuses on providing data security and protecting users’ privacy more effectively during different stages of the data pipeline. These stages include data collection, sharing, processing, and analytics, which may leak personal and sensitive information. We aim to provide acceptable data utility under strong security/privacy guarantees.

  • Hyper-scale Graph Computing

Our research focuses on large-scale graph representation learning and graph-based knowledge base technologies, as well as the underlying hyper-scale graph computing engines and hyper-scale knowledge base inference systems, with the purpose of contributing to the fields of information retrieval, distributed computing, large-scale system design, machine learning, artificial intelligence, and natural language processing.

Products and Applications
  • Hyper-scale Graph Inference Engines

    The data of the Alibaba ecosystem is extremely rich and varied, covering everything from shopping, travel, entertainment, and payment. Graph inference combined with deep learning has achieved successful phased results in many of Alibaba's business scenarios. Large-scale graph representations are emerging as ancillary information that can effectively use cross domain information. Thus, we can truly have better understanding the needs of consumers in different business scenarios. We are working on the development of a new generation of graph learning platform that can efficiently perform inference analysis on billions of nodes and trillions of edges.


  • Solutions to E-commerce Fraud Detection

    Internet fraud artifice arises as an endless stream.  Alibaba is also heavily attacked by the Internet blackmail, and anti-fraud has become one of its most important tasks. Alibaba can identify tens of millions of highly suspicious devices with their traffic flow every day.  Fraud detection can be roughly divided into two directions: channel equipment and traffic anti-fraud. The main task of channel devices anti-fraud is to identify suspicious simulators, equipment farms, etc. We extracted all kinds of sparse and dense features of the devices from various logs, and effectively modified Google's Wide & Deep model based on our business scenarios to identify millions of highly trusted fraudulent devices every day. The traffic anti-fraud is more related to business scenarios. By considering and modelling suspicious traffic as a whole with enhanced information, we can improve the model capability and effectively identify suspicious devices. Thus, we propose a series of graph models that are able to detect and intercept millions of cheating cookies with high accuracy from hyper-scale traffic logs every day.

Research Team
Jingren ZhouHead of Data Analytics and Intelligence Lab

IEEE Fellow. He holds a Ph.D. in Computer Science from Columbia University. From 2004 to 2015, he served as a researcher at Microsoft Research and an R&D partner at Microsoft. Dr. Zhou has published dozens of papers in top conferences and journals in the fields of large-scale distributed systems, query processing and the optimization of distributed databases, and holds several patented inventions of key technologies in the industry. His current research directions are data processing methods based on large-scale distributed systems and machine learning algorithm platforms.

Bolin DingSenior Staff Engineer of Data Analytics and Intelligence Lab

Dr. Bolin Ding completed his Ph.D. in Computer Science at University of Illinois at Urbana-Champaign. His research focuses on the management and analytics of large-scale data, including real-time approximate query algorithms and systems, data privacy protection, query processing and optimization algorithms, and algorithms and applications of data mining and machine learning. Prior to joining Alibaba, he worked as a researcher in Microsoft Research. He has hold more than 10 US patents. He received the 2017 Technical Excellence Award from Microsoft Privacy for his contributions on the research and deployment of data privacy techniques. He has published more than 50 papers in top conferences and journals in related areas, including SIGMOD, VLDB, ICDE, KDD, CHI, AAAI, and NIPS.

Zhengping QianSenior Staff Engineer of Data Analytics and Intelligence Lab

Director in the Computing Platform Team at Alibaba. He is responsible for driving the development of new systems and business solutions for emerging applications from both inside and outside Alibaba, such as low-latency graph analytics and machine learning. Before joining Alibaba in 2015, he was a Lead Researcher at Microsoft Research. His research interests are in distributed and data-parallel computing. He has published papers in top systems conferences (including OSDI, NSDI, EuroSys, and VLDB) and received the Best Paper Award from EuroSys 2012.Qian received his PhD in Computer Science from South China University of Technology in 2009.

Hongxia YangSenior Staff Data Scientist of Data Analytics and Intelligence Lab

She received her PhD degree in Statistics from Duke University in 2010. Her interests span the areas of Bayesian statistics, time series analysis, spatial-temporal modeling, survival analysis, machine learning, data mining and their applications to problems in business analytics and big data. She used to work as the Principal Data Scientist at Yahoo! Inc and Research Staff Member at IBM T.J. Watson Research Center respectively. She has published over 40 top conference and journal papers and is serving as the associate editor for Applied Stochastic Models in Business and Industry. She has been been elected as an Elected Member of the International Statistical Institute (ISI) in 2017.

Kai ZengStaff Engineer of Data Analysis and Intelligence Lab

Dr. Zeng received his Ph.D. in Computer Science from the University of California Los Angeles. Before joining Alibaba, he was a Senior Scientist at Microsoft Cloud and Information Service Lab, and a postdoc researcher at AMPLab, Univeristy of California Berkeley before that. He is committed to the research of large-scale distributed systems and database systems. He has published papers in top database journals and conferences (including SIGMOD, VLDB, ICDE, TODS, and so on). He has received the Best Paper Award in 2012 and the Best Demonstration Award in 2014 from SIGMOD and was nominated for this Best Demonstration Award in 2010.

Wenyuan YuSenior Staff Engineer of Data Analytics and Intelligence Lab

He received his Ph.D in Informatics from University of Edinburgh. Prior to joining Alibaba, he was the CEO of 7bridges Ltd, and a research scientist at Facebook. He is a recipient of the SIGMOD best paper award in 2017, the VLDB best demo award in 2017 and the VLDB best paper award in 2010. His research interests include data quality management and graph data processing.

Academic Achievements
  • Yang, H. and Zhou, J., The Study of Cognitive Graph and its Practice in E-Commerce, Communications for China Computer Federation. CCCF 2020.
  • Ji, Yu., Yin, M., Yang, H., Zhou, J., Zheng, V., Shi, C. and Fang, Y. Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling, ACM Transactions on Knowledge Discovery from Data. TKDD 2020.
  • Wenfei Fan, Ping Lu, Chao Tian. Unifying logic rules and machine learning for entity enhancing. SCIC 2020.
  • Wenfei Fan, Xueli Liu, Ping Lu, Chao Tian. Catching Numeric Inconsistencies in Graphs. TOIS 2020.
  • Yuan Zhang, Fei Sun, Xiaoyong Yang, Chen Xu, Wenwu Ou and Yan Zhang. 2020. Graph-based Regularization on Embedding Layers for Recommendation. ACM Transactions on Information Systems. TOIS 2020.
  • Wenfei Fan, Ping Lu, Wenyuan Yu, Jingbo Xu, Qiang Yin, Xiaojian Luo, Jingren Zhou, and Ruochun Jin. 2020. Adaptive Asynchronous Parallelization of Graph Algorithms. ACM Transactions on Database Systems. TODS 2020.
  • Yongxin Tong, Yuxiang Zeng, Bolin Ding, Libin Wang, and Lei Chen. Two-Sided Online Micro-Task Assignment in Spatial Crowdsourcing. IEEE Transactions on Knowledge and Data Engineering. TKDE 2019.
  • Yukuo Cen , Jing Zhang, Hongxia Yang and Jie Tang, Trust Prediction in Alibaba E-Commerce Platform, ACM Transactions on Knowledge and Data Engineering. TKDE 2019.
  • Zhaojing Luo, Shaofeng Cai, Jinyang Gao, Meihui Zhang, Kee Yuan Ngiam, Gang Chen, Wang-Chien Lee. Improving Data Analytics with Fast and Adaptive Regularization. TKDE 2019.
  • Yu Zhu, Jinghao Lin, Shibi He, Beidou Wang, Ziyu Guan and Deng Cai, Addressing the Item Cold-start Problem by Attribute-driven Active Learning, the IEEE Transactions on Knowledge and Data Engineering, 2019, to appear. TKDE 2019
  • Yang Yu, Shi-Yong Chen, Qing Da, and Zhi-Hua Zhou, Reusable Reinforcement Learning via Shallow Trails, the IEEE Transactions on Neural Networks and Learning Systems. TNNLS 2018.
  • Ding, M., Zhou, C., Yang, H. and Tang, J., CogLTX: Applying BERT to Long Texts. Neural Information Processing Systems. NeurIPS 2020.
  • Zou, H., Cui, P., Ma, J. and Yang, H., Counterfactual Prediction for Bundle Treatment. Neural Information Processing Systems. NeurIPS 2020.
  • Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu. Learning to Mutate with Hypergradient Guided Population. Neural Information Processing Systems. NeurIPS 2020.
  • Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen. Scalable Graph Neural Networks via Bidirectional Propagation. Neural Information Processing Systems. NeurIPS 2020.
  • Yufei Feng, Fuyu Lv, Binbin Hu, Fei Sun, Kun Kuang, Yang Liu, Qingwen Liu, Wenwu Ou. MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction. International Conference on Information and Knowledge Management. CIKM 2020.
  • Daoyuan Chen, Yaliang Li, Bolin Ding, and Ying Shen. An Adaptive Embedding Framework for Heterogeneous Information Networks. International Conference on Information and Knowledge Management. CIKM 2020.
  • Ji, Y., Yin, M., Fang, Y., Yang, H., Wang, X. and Shi, C., Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation. ECML-PKDD 2020.
  • Wang, Y., Pan, G., Yao, Y., Tong, H., Yang, H., Xu, F. and Lu, J., Bringing Order to Network Embedding: A Relative Ranking based Approach. 29th ACM International Conference on Information and Knowledge Management. CIKM 2020.
  • Zhang, S., Tan, Z., Yu, J., Zhao, Z., Kuang, K., Zhou, J., Yang, H. and Wu, F., Poet: Product-oriented Video Captioner for E-commerce. 28th ACM International Conference on Multimedia. MM 2020.
  • Zhang, S., Jiang, T., Wang, T., Kuang, K, Yu, J., Yang, H. and Wu, F., DeVLBert: Learning Deconfounded Visio-Linguistic Representations. 28th ACM International Conference on Multimedia. MM 2020.
  • Chu, Y., Wang, X., Ma, J., Jia, K., Zhou, J. and Yang, H., Inductive Granger Causal Modeling for Multivariate Time Series. IEEE International Conference on Data Mining series. ICDM 2020.
  • Tianhao Wang, Bolin Ding, Min Xu, Zhicong Huang, Cheng Hong, Jingren Zhou, Ninghui Li, and Somesh Jha. Improving Utility and Security of the Shuffler-based Differential Privacy. VLDB 2021.
  • Zuozhi Wang, Kai Zeng, Botong Huang, Wei Chen, Xiaozong Cui, Bo Wang, Ji Liu, Liya Fan, Dachuan Qu, Zhenyu Hou, Tao Guan, Chen Li, Jingren Zhou. Beanstalk: A General Cost-Based Optimizer Framework for Incremental Data Processing. VLDB 2020.
  • Wenfei Fan, Ruochun Jin, Muyang Liu, Ping Lu, Chao Tian, Jingren Zhou. Capturing Associations in Graphs. VLDB 2020.
  • Wenfei Fan, Muyang Liu, Chao Tian, Ruiqi Xu, Jingren Zhou. Incrementalization of Graph Partitioning Algorithms. VLDB 2020.
  • Xiaowei Jiang, Yuejun Hu, Yu Xiang, Guangran Jiang, Xiaojun Jin, Chen Xia, Weihua Jiang, Jun Yu, Haitao Wang, Yuan Jiang, Jihong Ma, Li Su, Kai Zeng. Alibaba Hologres: A Cloud-Native Service for Hybrid Serving/Analytical Processing. VLDB 2020.
  • Min Xu, Bolin Ding, Tianhao Wang, and Jingren Zhou. Collecting and Analyzing Data Jointly from Multiple Services under Local Differential Privacy. VLDB 2020.
  • Zuozhi Wang, Kai Zeng, Botong Huang, Wei Chen, Xiaozong Cui, Bo Wang, Ji Liu, Liya Fan, Dachuan Qu, Zhenyu Ho, Tao Guan, Chen Li, Jingren Zhou. Grosbeak: A Data Warehouse Supporting Resource-Aware Incremental Computing. SIGMOD 2020.
  • Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. Simple and Deep Graph Convolutional Networks. ICML 2020.
  • Haoji Hu, Jinyang Gao and Xiangnan He, Modeling Personalized Item Frequency Information for Next-basket Recommendation. SIGIR 2020.
  • Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Xin Zhao, Hui Wang, Bolin Ding and Jirong Wen, Sequential Recommendation with Self-Attentive Multi-Adversarial Network. SIGIR 2020.
  • Chen Xu, Quan Li, Junfeng Ge, Jinyang Gao, Xiaoyong Yang, Changhua Pei, Fei Sun, Jian Wu, Hanxiao Sun, and Wenwu Ou: Privileged Features Distillation at Taobao Recommendations. KDD 2020.
  • Bingqing Lyu, Lu Qin, Xuemin Lin, Ying Zhang, Zhengping Qian, Jingren Zhou: Maximum Biclique Search at Billion Scale. VLDB 2020.
  • Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei Lin, and Jingren Zhou. AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search. IJCAI 2020.
  • Zhaoyang Liu, Haokun Chen, Fei Sun, Xu Xie, Jinyang Gao, Bolin Ding, and Yanyan Shen. Intent Preference Decoupling for User Representation on Online Recommender System. IJCAI 2020.
  • Ya Zhao,Rui Xu,Xinchao Wang, Peng Hou , Haihong Tang ,Mingli Song. Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers. AAAI 2020.
  • Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, and Ji-Rong Wen. Sequential Recommendation with Self-attentive Multi-adversarial Network. SIGIR 2020.
  • Zhuolun Xiang, Bolin Ding, Xi He, and Jingren Zhou. Linear and Range Counting under Metric-based Local Differential Privacy. ISIT 2020.
  • Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, and Bolin Ding. Towards Differentially Private Truth Discovery for Crowd Sensing Systems. ICDCS 2020.
  • Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, and Zhenhui Li. Automated Relational Meta-learning. ICLR 2020.
  • Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, and Philip S. Yu. Entity Synonyms Discovery via Multipiece Bilateral Context Matching. IJCAI 2020.
  • Yuexiang Xie, Ying Shen, Yaliang Li, Min Yang, and Kai Lei. Attentive User-Engaged Adversarial Neural Network for Community Question Answering. AAAI 2020.
  • Daoyuan Chen, Yaliang Li, Kai Lei, and Ying Shen. Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents. ACL 2020.
  • Jinyang Gao, Junjie Yao, Yingxia Shao. Towards Reliable Learning for High Stakes Applications. AAAI 2019.
  • Shiwen Wu, Xu Xie, Jinyang Gao, Bin Cui. Enhanced Review-based Rating Prediction by Exploiting Aside Information and User Influence. AAAI 2020.
  • Yang Li, Jinyang Gao, Jiawei Jiang, Yingxia Shao, Bin Cui. Efficient Automatic CASH via Rising Bandits. AAAI 2020.
  • Xiaotian Hao, Junqi Jin, Jin Li, Weixun Wang, Yi Ma, Jianye Hao, Zhenzhe Zheng, Han Li, Jian Xu, Kun Gai. Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising. IJCAI 2020.
  • Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Weinan Zhang, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai. Sequential Advertising Agent with Interpretable User Hidden Intents. AAMAS 2020.
  • Hengtong Zhang, Yaliang Li, Bolin Ding, and Jing Gao. Practical Data Poisoning Attack Against Next-Item Recommendation. WWW 2020.
  • Wenfei Fan, Ruochun Jin, Muyang Liu, Ping Lu, Xiaojian Luo, Ruiqi Xu, Qiang Yin, Wenyuan Yu and Jingren Zhou. Application Driven Graph Partitioning. SIGMOD 2020.
  • Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Hongxia Yang, Fei Wu and Jingren Zhou: Comprehensive Information Integration Modeling Framework for Video Titling. KDD 2020.
  • Jianxin Ma, Chang Zhou, Hongxia Yang, Cui Peng, Xin Wang and Wenwu Zhu: Disentangled Self-Supervision in Sequential Recommenders. KDD 2020.
  • Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang and Jie Tang: Controllable Multi-Interest Framework for Recommendation. KDD 2020.
  • Jiezhong Qiu, Qibin Chen, Hongxia Yang, Ming Ding, Kuansan Wang and Jie Tang: Graph Contrastive Coding for Structural Graph Representation Pre-Training. KDD 2020.
  • Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou and Jie Tang: Understanding Negative Sampling in Graph Representation Learning. KDD 2020.
  • Yue He, Peng Cui, Jianxin Ma, Zou Hao, Xiaowei Wang, Hongxia Yang and Philip S. Yu: Learning Stable Graphs from Heterogeneous Confounded Environments. KDD 2020.
  • Qiaoyu Tan, Ninghao Liu, Hongxia Yang, Jingren Zhou and Xia Hu: Learning to Hash with Graph Neural Networks for Recommender Systems. WWW 2020.
  • Zhuoren Jiang, Zheng Gao, Jinjiong Lan, Hongxia Yang, Yao Lu and Xiaozhong Liu: Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph Embedding. WWW 2020.
  • Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhao Li and Can Wang. Learning Temporal Interaction Graph Embedding via Coupled Memory Networks. WWW 2020.
  • Hongrui Zhao, Jin Yu, Donghui Wang, Jie Liu, Hongxia Yang and Fei Wu: Dress like an Internet Celebrity: Fashion Retrieval in Videos. IJCAI 2020.
  • Wentao Huang, Yuchen Li, Yuan Fang and Hongxia Yang: BiANE: Bipartite Attributed Network Embedding. SIGIR 2020.
  • Di Yin, Jiwei Tan, Zhe Zhang, Hongbo Deng, Shujian Huang and Jiajun Chen, Learning to Generate Personalized Query Auto-Completions via a Multi-View Multi-Task Attentive Approach. KDD 2020.
  • Peng Zhang, Kefeng Ning, Wenxiang Zhu, Yu Zhang, Chuanren Liu: Prediction and Profiling of Audience Competition for Online Television Series. KDD 2020.
  • Jiarui Jin, Yuchen Fang, Weinan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai. A Deep Recurrent Survival Model for Unbiased Ranking. SIGIR 2020.
  • Hongrui Zhao, Jin Yu, Donghui Wang, Jie Liu, Hongxia Yang and Fei Wu: Dress like an Internet Celebrity: Fashion Retrieval in Videos. IJCAI 2020.
  • Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng and Aixin Sun. CATN: Cross-Domain. SIGIR 2020.
  • Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun and Hongbo Deng. ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance. SIGIR 2020.
  • Xusheng Luo, Luxin Liu, Yonghua Yang, Le Bo, Yuanpeng Cao, Jinhang Wu, Qiang Li, Kepin Yang and Kenny Q. Zhu. AliCoCo: Alibaba E-commerce Cognitive Concept Net. SIGMOD 202.
  • Tong Zhang, Baoqliang Cui, Zhen Cui, Haikuan Huang,Jian Yang,Hongbo Deng, Bo Zheng Cross-Graph Convolution Learning for Large-Scale Text-Picture Shopping Guide in E-Commerce Search. ICDE 2020.
  • Zhao Li, Xin Sheng, Yuhang Jiao, Xuming Pan, Pengcheng Zou, Xianling Meng, Chenwei Yao, Jiajun Bu. Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commence Applications. ICDE 202.
  • Junshuai Song, Zhao Li, Zehong Hu, Jun Gao. PoisonRec: An Adaptive Data Poisoning Framework for Attacking Black-box Recommender Systems. ICDE 2020.
  • ianhao Wang, Bolin Ding, Jingren Zhou, Cheng Hong, Zhicong Huang, Ninghui Li, and Somesh Jha. Answering Multi-Dimensional Analytical Queries under Local Differential Privacy. SIGMOD 2019.
  • Cedric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, and Ce Zhang. Continuous Integration of Machine Learning Models: A Rigorous Yet Practical Treatment. SysML 2019.
  • Wei Wang, Jinyang Gao, Meihui Zhang, Sheng Wang, Gang Chen, Teck Khim Ng, Beng Chin Ooi, Jie Shao, Moaz Reyad. Rafiki: Machine Learning as an Analytics Service System. VLDB 2019.
  • Shaofeng Cai, Gang Chen, Beng Chin Ooi, Jinyang Gao. Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints. VLDB 2010.
  • Kaiping Zheng, Wei Wang, Jinyang Gao, Meihui Zhang, Beng Chin Ooi. OPCM: Optimizing Task Decomposition for Reliable Learning. VLDB 2020.
  • Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yan and Wenwu Zhu. Learning Disentangled Representations for Recommendation. NeurIPS 2019.
  • Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai. Joint Optimization of Tree-based Index and Deep Model for Recommender Systems. NeurIPS 2019.
  • Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, and Sergey Yekhanin. An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. NeurIPS 2019.
  • Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang and Jie Tang. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. ACL 2019.
  • Xun Yang, Yasong Li, Hao Wang, Di Wu, Qing Tan, Jian Xu, Kun Gai. Bid Optimization by Multivariable Control in Display Advertising. KDD 2019.
  • Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma and Yongliang Li:Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. KDD 2019.
  • Jingwen Ye, Yixin Ji, Xinchao Wang, Kairi Ou, Dapeng Tao, Mingli Song.Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation. CVPR 2019.
  • Anxiang Zeng, Han Yu, Xin Gao, Kairi Ou, Zhenchuan Huang, Peng Hou, Mingli Song, Jingshu Zhang and Chunyan Miao. An Online Intelligent Visual Interaction System PDF. IJCAI 2019.
  • Qianlong Liu, Baoliang Cui, Zhongyu Wei, Baolin Peng, Haikuan Huang, Hongbo Deng, Jianye Hao, Xuanjing Huang and Kam-fai Wong. Building Personalized Simulator for Interactive Search. IJCAI 2019.
  • Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan, PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation. IJCAI 2019.
  • Qiwei Chen, Huan Zhao,Wei Li, Pipei Huang, Wenwu Ou. Behavior Sequence Transformer for E-commerce Recommendation in Alibaba. KDD 2019.
  • Changhua Pei and Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, Dan Pei. Personalized Re-ranking for Recommendation. ReSys 2019.
  • Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, Peng Jiang. A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation. RexSys 2019.
  • Xuanwu Liu, Zhao Li, Jun Wang, Guoxian Yu, Carlotta Domeniconi, and Xiangliang Zhang. Cross-modal Zero-shot Hashing. ICDM 2019.
  • Shen Xin, Martin Ester, Jiajun Bu, Chengwei Yao, Zhao Li, Xun Zhou, Yizhou Ye and Can Wang. Multi-task based Sales Predictions for Online Promotions. ICDM 2019.
  • Longbin Lai, Zhu Qing, Zhengyi Yang, Xin Jin, Zhengmin Lai, Ran Wang, Kongzhang Hao, Xuemin Lin, Lu Qin, Wenjie Zhang, Ying Zhang, Zhengping Qian, Jingren Zhou. Distributed subgraph matching on timely dataflow. VLDB 2019.
  • Min Xu, Tianhao Wang, Bolin Ding, Jingren Zhou, Cheng Hong, and Zhicong Huang. DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy. VLDB 2019.
  • Yang Deng, Yaliang Li, Nan Du, Wei Fan, Ying Shen, Min Yang, and Kai Lei. MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data. CIKM 2019.
  • Yu Zhu, Yu Gong, Qingwen Liu, Yingcai Ma, Wenwu Ou, Junxiong Zhu, Beidou Wang, Ziyu Guan, and Deng Cai. Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU. CIKM 2019.
  • Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou and Peng Jiang. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. CIKM 2019.
  • Dagui Chen, Junqi Jin, Weinan Zhang, Fei Pan, Lvyin Niu, Chuan Yu, Jun Wang, Han Li, Jian Xu, Kun Gai. Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds. CIKM 2019.
  • Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Weinan Zhang, Jun Wang, Yixi Wang, Han Li, Jian Xu, Kun Gai. Learning Adaptive Display Exposure for Real-Time Advertising. CIKM 2019.
  • Kan Ren, Jiarui Qin, Yuchen Fang, Weinan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai. Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction. SIGIR 2019.
  • Fei Xiao, Zhen Wang, HaiKuan Huang, et al. AliISA: Creating an Interactive Search Experience in E-commerce Platforms[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2019: 1305-1308. SIGIR 2019.
  • Bo Wang, Minghui Qiu, Xisen Wang, Yaliang Li, Yu Gong, Xiaoyi Zeng, Jun Huang, Bo Zheng, Deng Cai, Jingren Zhou: A Minimax Game for Instance based Selective Transfer Learning. KDD 2019.
  • Minghui Qiu, Bo Wang, Cen Chen, Xiaoyi Zeng, Jun Huang, Deng Cai, Jingren Zhou and Forrest Sheng Bao: Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search. CIKM 2019.
  • Yatao Yang, Jun Tan, Hongbo Deng, Zibin Zheng, Yutong Lu, Xiangke Liao: An Active and Deep Semantic Matching Framework for Query Rewrite in E-Commercial Search Engine. CIKM 2019.
  • Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, Binqiang Zhao: POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion. KDD 2019.
  • Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Pipei Huang, Huan Zhao, Guoliang Kang, Qiwei Chen, Wei Li, Dik Lun Lee: Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. CIKM 2019.
  • Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Guan Ziyu, Fei Sun, Wenwu Ou, Kenny Q. Zhu: Exact-K Recommendation via Maximal Clique Optimization. KDD 2019.
  • Xusong Chen, Chenyi Lei, Dong Liu, Rui Li, Zheng-Jun Zha and Zhiwei Xiong, BERT4SessRec: Content-Based Video Relevance Prediction with Bidirectional Encoder Representations from Transformer. ACM Multimedia 2019.
  • Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, Wilfred Ng, SDM: Sequential Deep Matching Model for Online Large-scale Recommender System. CIKM 2019.
  • Rong Xiao, Jianhui Ji, Baoliang Cui, Haihong Tang, Wenwu Ou, Yanghua Xiao, Jiwei Tan, Xuan Ju: Weakly Supervised Co-Training of Query Rewriting and Semantic Matching for e-Commerce. WSDM 2019.
  • Xusheng Luo, Yonghua Yang, Kenny Zhu, Yu Gong and Keping Yang, Conceptualize and Infer User Needs in E-commerce. CIKM 2019.
  • Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, Jun Gao, AddGraph: Anomaly Detection in Dynamic Graph using Attention-based Temporal GCN. IJCAI 2019.
  • Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang, ActiveHNE: Active Heterogeneous Network Embedding. IJCAI 2019.
  • Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, Keping Yang, Deep Session Interest Network for Click-Through Rate Prediction. ICJCAI 2019.
  • Jing He , Meng Han, Shouling Ji, Tianyu Du, and Zhao Li, Spreading Social Influence with both Positive and Negative Opinions in Online Networks. BDMA 2019.
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