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Workshop on Decision Making for Information Retrieval and Recommendation System (Jointly with the Web Conference'23) Webpage: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdecisionmaking4ir.github.io%2FWWW-2023%2F&data=05%7C01%7Cuai%40engr.oregonstate.edu%7Caa05ffcd59264843791408db0876a39b%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638113077860968872%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=MILc5UMW70vca8S3u7tKZbd%2B%2FDCSksxS95wwMI2qMqY%3D&reserved=0 Location: Austin, Texas, USA Date: April 30 and May 1, 2023 Call for Paper 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 * Submission deadline: Feb 20, 2023 (extended from Feb 6) * Notification of final decisions: Mar. 6, 2023 * Camera-ready version submission: Mar 20, 2023 * Workshops at WWW’23: April 30 and May 1, 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://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Feasychair.org%2Fconferences%2Fsubmissions%3Fa%3D29997336&data=05%7C01%7Cuai%40engr.oregonstate.edu%7Caa05ffcd59264843791408db0876a39b%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638113077860968872%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=54ej69IBZ%2FcEFJkWu5Cgq5bxz6QtnoWM5mLNZ0CZvms%3D&reserved=0) Organizers: Da Xu (LinkedIn), Tobias Schnabel (Microsoft Research), Xiquan Cui (Home Depot), Sarah Dean (Cornell University), Jianpeng Xu (Walmart Labs), Aniket Deshmukh (Amazon), Bo Yang (Amazon).
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