Most IR systems can be described by a pattern recognition stage and a decision-making stage. In the past decade, the flourishing of deep learning has provided IR with unprecedented opportunities for mining complex signals from collected data, shifting the public focus vastly toward the pattern recognition end. However, making predictions based on the discovered patterns is merely the first step toward information retrieval: the agent must strategically decide how to leverage the predictions to maximize the various utilities of different parties. Proposed in a timely manner, this workshop aims to bring together researchers and practitioners from academia and industry for the critical challenges of transitioning the pattern recognition outcome to effective decision making in IR. Crucially, the decision making strategy will have a considerable impact on the immediate performance and long-term development of real-world web search and data mining applications.

We wish to unite researchers and practitioners from various backgrounds to identify the emerging challenges, discover the connections from various domains, and study promising solutions of decision making for modern infromation retrieval systems. We note that the decision making strategy may impact either the immediate performance and long-term development of real-world web search and data mining applications. We also pay special attention to the role of human involvement in practical decision making, and motivate novel applications and business incentives.

Specific topics of interest are including but not limited to:

Emerging challenges in decision making for modern IR systems, including search, recommendation and advertising;
User-centric evaluation of decision making with IR systems;
Frameworks or end-to-end solutions for industrial large-scale IR decision making;
Designing and optimizing online or user experiments for IR systems;
Online (sequential) decision making with bandits and reinforcement learning;
Robust & uncertainty-aware decision making for IR;
Practical causal inference & counterfactual reasoning for decision making in IR;
Connecting the tools and theories for decision making from other domains, e.g. marketing, econometric and statistics, to IR problems;
Modern theory of decision making;
Existing or novel applications relevant to decision making in IR;
Multi-objective or multi-modality problems of decision making;
Conformal inference, online & multiple hypothesis testing, and false discovery control;
Submission Directions:

The workshop accepts long papers (limited to 8 pages), short papers (4 pages), posters, abstracts and demos (2 pages), excluding references and appendix. Paper submission and reviewing will be following the directions of the WSDM main conference. Reviews are not double-blind, and author names and affiliations should be listed. Submissions should include all content and references within the limited pages, and must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. For LaTeX users: unzip acmart.zip, make, and use sample-sigconf.tex as a template. Additional information about formatting and style files is available online at: https://www.acm.org/publications/proceedings-template. Papers that do not meet the formatting requirements will be rejected without review. In addition, authors can provide an optional two (2) page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. For detailed information, please visit https://decisionmaking4ir.github.io/WSDM-2022/