About Me

Qian Yu is now working as an algorithm expert in JD.com. His current research interests include sequential recommendation, natural language processing and data augmentation. Prior to joining JD.com at August 2020, he obtained his PhD degree at the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, under the supervision of Prof. Wai Lam. He received BE and MS degree (supervised by Prof. Yuexian Hou) on Software Engineering from Tianjin University, China.

  • Room 501, William M.W. Mong Engineering Building,
    The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
  • q.yuoutlook.com
    yuqianlink.cuhk.edu.hk
  • Personal page in CUHK
    Page in Gooble Scholar
  • Data Augmentation, Text Generation, Generative Probabilistic Models Sequence Modeling, Sequential Recommendation
    Information Retrieval, Natural Language Processing

Activities & Awards

Selected

  • 2019.01 ~ 2020.06, research intern, Machine Intelligence, Alibaba DAMO Academy
  • 2015.04 ~ 2015.12, research assistant, Department of Computing, The Hong Kong Polytechnic University
  • 2011, scholarship, Standard Chartered TEDA-Scope Scholarship
  • 2011, team leader, The 3rd Prize in SCS Topnotch Innovation Fund Project, TJU
  • 2010, MCU coder, Excellent Performance Award in Robot Athletic Competition, TJU
  • 2009, voluntary teaching assistant, Tianjin School for the Visually Impaired

Publications

  • Chen Yang, Jin Chen, Qian Yu, Xiangdong Wu, et al. An Incremental Update Framework for Online Recommenders with Data-Driven Prior. The ACM International Conference on Information and Knowledge Management (CIKM), pp. 4894-4900, 2023.
  • Zihao Fu, Wai Lam, Qian Yu, Anthony Man-Cho So, Shengding Hu, Zhiyuan Liu, Nigel Collier. Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder. 2023.
  • Yang Deng, Wenxuan Zhang, Qian Yu, Wai Lam. Product Question Answering in E-Commerce: A Survey. The Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
  • Jinbo Song, Ruoran Huang, Xinyang Wang, Wei Huang, Qian Yu, Mingming Chen, Yafei Yao, Chaosheng Fan, Changping Peng, Zhangang Lin, Jinghe Hu, Jingping Shao. Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models. The ACM International Conference on Information and Knowledge Management (CIKM), pp. 4495-4499, 2022.
  • Xiaoxiao Xu, Zhiwei Fang, Qian Yu, Ruoran Huang, Yong Li, Yang He, Changping Peng, Zhangang Lin, Jingping Shao. Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction. The International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), pp. 1890-1894, 2022.
  • Xiaoxiao Xu, Chen Yang, Qian Yu, Zhiwei Fang, Jiaxing Wang, Chaosheng Fan, Yang He, Changping Peng, Zhangang Lin, Jingping Shao. Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework. The ACM Web Conference (WWW), pp. 27-35, 2022.
  • Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si. IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks. The Annual Meeting of the Association for Computational Linguistics (ACL), pp. 2277-2287, 2022.
  • Qian Yu, Xiangdong Wu, Chen Yang, Zihao Zhao, Haoxin Liu, Chaosheng Fan, Changping Peng, Zhangang Lin, Jinghe Hu, Jingping Shao. Exploiting Global Behavior Contextual Correlation in Sequential Recommendation Augmentation. The ACM International Conference on Information and Knowledge Management Workshop on Deep Learning for Search and Recommendation (CIKM-DL4SR), 2022.
  • Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si. Review-based Question Generation with Adaptive Instance Transfer and Augmentation. The Annual Meeting of the Association for Computational Linguistics (ACL), pp. 280-290, 2020.
  • Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si. APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning. The Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7000-7011, 2020.
  • Wenxuan Zhang, Qian Yu, Wai Lam. Answering Product-related Questions with Heterogeneous Information. The Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL), pp. 696-705, 2020.
  • Qian Yu, Wai Lam. Data Augmentation based on Adversarial Autoencoder Handling Imbalance for Learning to Rank. AAAI Conference on Artificial Intelligence (AAAI), pp. 411-418, 2019.
  • Qian Yu, Wai Lam. Review-Aware Answer Prediction for Product-Related Questions Incorporating Aspects. ACM International Conference on Web Search and Data Mining (WSDM), pp. 691-699, 2018.
  • Qian Yu, Wai Lam, Zihao Wang. Responding E-commerce Product Questions via Exploiting QA Collections and Reviews. International Conference on Computational Linguistics (COLING), pp. 2192-2203, 2018.
  • Qian Yu, Wai Lam. Product Question Intent Detection using Indicative Clause Attention and Adversarial Learning. ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR), pp. 75-82, 2018.
  • Yinqing Xu, Qian Yu*, Wai Lam, Tianyi Lin. Exploiting Interactions of Review Text, Hidden User Communities and Item Groups, and Time for Collaborative Filtering. Knowledge and Information Systems (KAIS), 52(1):221-254, 2017.
  • Peng Zhang, Qian Yu, Yuexian Hou*, Dawei Song*, Jingfei Li, Bin Hu. A Distribution Separation Method Using Irrelevance Feedback Data for Inforamtion Retrieval. ACM Transactions on Intelligent Systems and Technology (TIST), 8(3):46, 2017.
  • Peng Zhang, Qian Yu, Yuexian Hou*, Dawei Song*, Jingfei Li, Bin Hu. Generalized Analysis of a Distribution Separation Method. Entropy, 18(4):105, 2016.
  • Qian Yu, Peng Zhang, Yuexian Hou, Dawei Song, Jun Wang. Document Boltzmann Machines for Information Retrieval. European Conference on Information Retrieval (ECIR), pp. 666-671, 2015.
  • Qian Yu, Yuexian Hou, Xiaozhao Zhao, Guochen Cheng. Rényi Divergence based Generalization for Learning of Classification Restricted Boltzmann Machines. IEEE International Conference on Data Mining Workshop on High Dimensional Data Mining (ICDM-HDM), pp. 692-697, 2014.
  • Guochen Cheng, Yuexian Hou, Xiaozhao Zhao, Qian Yu. Local and Non-local Regularization for Semi-Supervised Deep Learning. International Conference on Control Engineering and Automation (ICCEA), 2014.
  • Xiaozhao Zhao, Yuexian Hou, Qian Yu, Dawei Song, Wenjie Li. Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle. arXiv preprint arXiv:1302.3931, 2013.

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