Han Zhang Researcher Work at JD.com Graduated from Natural Language Processing Lab at Tsinghua University Advised by Maosong Sun Email: zhanghan33@jd.com Google scholar: Han Zhang Research Interests: Information Retrieval

Experience

• THUNLP Lab, Tsinghua University, Beijing, China
  Sep, 2015 - Jul, 2018.
  
  Master
  
• Dept. of Computer Science and Technology, Tongji University, Shanghai, China
  Sep, 2011 - Jul, 2015.
  
  Undergraduate student
  

Publications [Google Scholar]

2021

• Han Zhang, Hongwei Shen, Yiming Qiu, Yunjiang Jiang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, and Wen-Yun Yang. Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index. arXiv preprint arXiv:2105.03933 (2021). [pdf][code]
• Yiming Qiu, Kang Zhang, Han Zhang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, and Wen-Yun Yang. Query Rewriting via Cycle-Consistent Translation for E-Commerce Search. arXiv preprint arXiv:2103.00800 (2021). [pdf]
• Xinlin Xia, Shang Wang, Han Zhang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, and Wen-Yun Yang. SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search. arXiv preprint arXiv:2107.00525 (2021) [pdf]

  2020

• Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Weipeng Yan, and Wen-Yun Yang. Towards personalized and semantic retrieval: An end-to-end solution for e-commerce search via embedding learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2407-2416. 2020. [pdf]

  2017

• Han Zhang, Maosong Sun, Xiaochen Wang, Zhengyang Song, Jie Tang, and Jimeng Sun. Smart jump: Automated navigation suggestion for videos in moocs. In Proceedings of the 26th international conference on world wide web companion, pp. 331-339. 2017.

Projects

• Poeem
  Poeem is a library for efficient approximate nearest neighbor (ANN) search, which has been widely adopted in industrial recommendation, advertising and search systems. Apart from other libraries, such as Faiss and ScaNN, which build embedding indexes with already learned embeddings, Poeem jointly learn the embedding index together with retrieval model in order to avoid the quantization distortion. Consequentially, Poeem is proved to outperform the previous methods significantly, as shown in our SIGIR paper. Poeem is written based on Tensorflow GPU version 1.15, and some of the core functionalities are written in C++, as custom TensorFlow ops. It is developed by JD.com Search.
  

Awards

Sep. 2017	Siebel Scholars.
Jul. 2015	Shanghai Excellent Graduate.