SIGIR'20 Workshop on Deep Reinforcement Learning for Information Retrieval

July 30, 2020

Xi'an, China

INTRO

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.

CALL FOR PAPER

In this workshop, we invite professionals, researchers and technologists of all relevant fields to present the state-of-the-art development and applications, share their envisions about the future of information retrieval with decision making techniques like deep reinforcement learning.

We encourage submissions on a broad range of DRL for knowledge discovery in various domains. Topics of interest include but are not limited to theoretical aspects, algorithms, methods, applications, and systems, such as:

  • Deep reinforcement learning (DRL) methods in information retrieval scenarios
  • DRL for recommender systems
  • DRL for online advertising
  • DRL for query expansion and/or reformulation
  • DRL for ranking
  • DRL for item sampling in traning
  • DRL in social netoworks & graph mining
  • We invite the submission of full research papers (6~10 pages) and short papers (2~4 pages). Full research papers must be original and should not have been published previously or be under consideration for publication while being evaluated for this workshop. Short paper can be pubilished previously. Papers must be in English, in PDF format, and in the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website (use the sigconf proceedings template). We will follow a single-blind process and submissions will be evaluated by the program committee based on the quality of the work and its fit to the workshop themes. All the papers are required to be submitted via EasyChair system. Accepted FULL RESEARCH papers are plan to be published in ACM Digital Library proceedings and will be widely indexed. All questions about submissions should be emailed to wnzhang [at] sjtu.edu.cn or zhaoxi35 [at] msu.edu.

    IMPORTANT DATES

    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

    ORGANIZERS

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    Weinan Zhang Shanghai Jiao Tong University

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    Xiangyu Zhao Michigan State University

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    Li Zhao Microsoft Research Asia (MSRA)

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    Dawei Yin Baidu

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    Grace Hui Yang Georgetown University

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    Alex Beutel Google Brain