SIGIR'20 Workshop on Deep Reinforcement Learning for Information Retrieval

8:30 - 11:30, July 30, 2020

Xi'an, China


Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users’ information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem. Since the widely use of mobile applications, more and more information retrieval services have provided interactive functionality and products. Thus, learning from interaction becomes a crucial machine learning paradigm for interactive IR, which is based on reinforcement learning. With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information retrieval techniques, which could continuously update the information retrieval strategies according to users’ real-time feedback, and optimize the expected cumulative long-term satisfaction from users.

Our workshop a half-day workshop DRL4IR at SIGIR 2020, with the aim to provide a venue, which can bring together academia researchers and industry practitioners (i) to discuss the principles, limitations and applications of DRL for information retrieval, and (ii) to foster research on innovative algorithms, novel techniques, and new applications of DRL to information retrieval.


June 21, 2020: Workshop paper submission due (23:59, AoE Time)

July 12, 2020: Workshop paper acceptance notifications

July 19, 2020: Camera-ready deadline for workshop papers

July 30, 2020: Workshop Day


  • 8:30 - 8:40AM -- Opening & Welcome
  • Dr.Weinan Zhang, Associate Professor, Shanghai Jiao Tong University
  • 8:40 - 9:10AM -- Keynote 1: Practices on applying DRL to IR tasks (tentative)
  • Dr.Alex Beutel, Staff Research Scientist, Google Brain
  • 9:10 - 9:40AM -- Keynote 2: Some experience on learning in decision-related IR tasks
  • Dr.Yang Yu, Professor, Nanjing University
  • 9:40 - 9:55AM -- Paper Talk 1: Deep Reinforced Query Reformulation for Information Retrieval
  • Xiao Wang, Craig Macdonald and Iadh Ounis, Glasgow University
  • 9:55 - 10:10AM -- Paper Talk 2: Reinforcement Learning Meets Information Seeking: Dynamic Search Challenge
  • Zhiwen Tang and Grace Hui Yang, Georgetown University
  • 10:10 - 10:30AM -- Break
  • 10:30 - 11:00AM -- Keynote 3: Practical Study of Dispatching and Driver Repositioning in Didi Chuxing
  • Dr.Zhe Xu, Senior Staff Algorithm Engineer, Didi Chuxing
  • 11:00 - 11:15AM -- Paper Talk 3: User Behavior Retrieval for Click-Through Rate Prediction
  • Jiarui Qin, Shanghai Jiao Tong University
  • 11:15 - 11:30AM -- Paper Talk 4: Corpus Compression for Deep Reinforcement Learning in Natural Language Environments
  • Zhiwen Tang and Grace Hui Yang, Georgetown University


    Weinan Zhang Shanghai Jiao Tong University


    Xiangyu Zhao Michigan State University


    Li Zhao Microsoft Research Asia (MSRA)


    Dawei Yin Baidu


    Grace Hui Yang Georgetown University


    Alex Beutel Google Brain