Most of the recent progress in information retrieval (IR) and recommender systems has been fueled by deep learning. However, algorithmic advances on accurate predictions and improved user modeling are just a small part of designing considerations of a much larger system. IR and recommender systems differ from other machine learning domains because they are inherently part of an ecosystem -- in the simplest case, a world of items and users. In these ecosystems, system designers face a broad range of decisions -- e.g., how to balance popularity, which incentives should be given to which users, or what safeguards to put in place to ensure the platform thrives in the long-run.
Our workshop aims to unite interested scholars, researchers, practitioners and engineers from various industries and disciplines for a comprehensive discussion of emerging challenges and promising solutions. We hope to inspire research ideas, frameworks, applications, experiments, as well as business incentives. The topics of interest include but not limited to:
Emerging issues, challenges, and case studies on using decision-making strategies in information retrieval and recommender systems
User-centric metric and evaluation for decision making
Designing and optimizing online or user experiments for search and recommender systems
Theory and methodology for sequential decision making
Frameworks or end-to-end solutions for decision making in large-scale production systems
General topics on learning and inference with feedback systems
Human-in-the-loop development of decision-making strategies
Algorithmic accessibility, fairness, inclusiveness, and bias for information retrieval and recommender systems
Research proposals and problem statements for using techniques from other fields (e.g. econometrics, public health) to address search and recommendation problems
Simulation and synthetic data analysis for decision making

Important Dates
Open for Submission: Dec.15, 2022
Submission deadline: Feb.6, 2023
Notification of final decisions: Mar. 6, 2023
Camera-ready version submission: Mar 20, 2023
Workshops at WWW’23: May 1 and 2, 2023

Submission Guidelines:
All the accepted submissions will be presented at the workshop, either in oral sessions or the poster session, and will be included in the conference proceedings. We invite quality research contributions and application studies in different formats:
Original research papers, both long (limited to 8 content pages) and short (limited to 4 content pages)
Extended abstracts for vision, perspective, and research proposal (4 content pages)
Posters or demos on decision making systems (4 content pages)
Workshop papers that have been previously published or are under review for another journal, conference or workshop should not be considered for publication. Workshop papers should not exceed 12 pages in length (maximum 8 pages for the main paper content + maximum 2 pages for appendixes + maximum 2 pages for references). Papers must be submitted in PDF format according to the ACM template published in the ACM guidelines, selecting the generic “sigconf” sample. The reviewing process is double-blinded, and authors can submit the manuscripts via Easychair (https://easychair.org/conferences/submissions?a=29997336).

Organizers:
Da Xu (LinkedIn)
Tobias Schnabel (Microsoft Research)
Xiquan Cui (Home Depot)
Sarah Dean (Cornell University)
Jianpeng Xu (Walmart Labs)
Aniket Deshmukh (Microsoft)
Bo Yang (Amazon)

For any further questions and information, please contact Da Xu (daxu5180@gmail.com).