DRL4IR: 3rd Workshop on Deep Reinforcement Learning for Information Retrieval

Information retrieval (IR) systems have become an essential component in modern society to help users find useful information, which consists of a series of processes including query expansion, item recall, item ranking and re-ranking, etc. Based on the ranked information list, users can provide their feedbacks. Such an interaction process between users and IR systems can be naturally formulated as a decision-making problem, which can be either one-step or sequential. In the last ten years, deep reinforcement learning (DRL) has become a promising direction for decision-making, since DRL utilizes the high model capacity of deep learning for complex decision-making tasks. Recently, there have been emerging research works focusing on leveraging DRL for IR tasks. However, the fundamental information theory under DRL settings, the principle of RL methods for IR tasks, or the experimental evaluation protocols of DRL-based IR systems, has not been deeply investigated. To this end, we propose the third DRL4IR workshop (https: //drl4ir.github.io) at SIGIR 2022, which provides a venue for both academia researchers and industry practitioners to present the recent advances of DRL-based IR system, to foster novel research, interesting findings, and new applications of DRL for IR. In the last two years, DRL4IR organized at SIGIR'20/21 was one of the most successful workshops and attracted over 200 workshop attendees each year. In this year, we will pay more attention to fundamental research topics and recent application advances, with an expectation of over 300 workshop participants.