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 aims 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.
CITATION STYLE
Zhang, W., Zhao, X., Zhao, L., Yin, D., Yang, G. H., & Beutel, A. (2020). Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances. In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2468–2471). Association for Computing Machinery, Inc. https://doi.org/10.1145/3397271.3401467
Mendeley helps you to discover research relevant for your work.