A forum to galvanize multidisciplinary research on recommender ecosystems, including: recommender systems, game theory, generative models, social dynamics, and reinforcement learning.
Submission Portal Opens: September 22, 2023Â
Submission Deadline: Friday, December 1st, 2023 (AOE) (Previously Monday, November 20, 2023)
Author Notification Deadline: Monday, December 11, 2023
Camera-ready deadline: Thursday, February 1, 2024
Workshop Date & Location: Monday, February 26 at the Vancouver Convention Centre
The workshop centers on the multi-faceted landscape of Recommender Ecosystems (RESs), which couple the behaviors of users, content providers, vendors, and advertisers to determine the long-term behavior of the platform. While prevalent in numerous online products, the modeling, learning, and optimization technologies traditionally used in recommenders prioritize interactions with a single user. Recent research has delved into multi-agent dynamics and economic interactions within RESs, encompassing areas like fairness, popularity bias, market design, social dynamics, and more. Despite its significance, this research remains fragmented across various academic domains. This workshop aspires to bridge these communities, emphasizing the convergence of diverse topics like game-theoretic models, AI techniques, and social dynamics to holistically comprehend RES ecosystems. By fostering interdisciplinary dialogue, the workshop aims to spotlight the complexities of RESs, engendering fresh insights and solutions.Â
Topics of interest include, but are not limited to:
Game-theoretic models and mechanism design in recommender systemsÂ
State of the art techniques, like generative models or reinforcement learning, for promoting the health and diversity of recommender ecosystems.
Social dynamics, filter bubbles, and polarization in recommender systemsÂ
Fairness and bias in recommender systems.
Multi-stakeholder recommendation, including users, businesses, and advertisersÂ
Understanding user/creator/vendor behavior and incentives in recommender ecosystems, and their interactions.
Interdisciplinary approaches to the study of agent interactions in recommender systems (e.g., incorporating behavioral psychology, sociology, and economics insights).
Submissions should be no more than 4 (four) pages long, excluding references, and follow AAAI'24 template. Submissions are double-blind (author identity shall not be revealed to the reviewers), so the submitted PDF file should not include any identifiable information of authors. An optional appendix of any length is allowed and should be put at the end of the paper (after references).
Submissions are collected on OpenReview at the following link: https://openreview.net/group?id=AAAI.org/2024/Workshop/EcoSys. Due to the short timeline, we will not have a rebuttal period, but the authors are encouraged to interact and discuss with reviewers on OpenReview after the acceptance notifications are sent out. Rejected papers and their reviews will remain private and not posted in public.
For questions, please contact: guytenn [at] google.com
Our workshop does not have formal proceedings, i.e., it is non-archival. Accepted papers will be available in public on OpenReview. Revisions to accepted papers will be allowed until shortly before the workshop date. We welcome submissions of unpublished papers, including those that are submitted to other venues if that other venue allows so. We allow papers that have been recently accepted to another venue. Specifically, papers that have been accepted to NeurIPS'23 main conference can be resubmitted to this workshop.
Professor and Information Science Science Department Chair at the University of Colorado
Associate Professor in the EECS Department at UC Berkeley
Assistant Professor of Operations Research and Statistics at MIT
Assistant Professor in the CS Department at the University of Chicago
Assistant Professor in the EECS Faculty at MIT
Postdoctoral Researcher at the Center for Language and Intelligence at Princeton University
Â
Assistant Professor at Technion
Assistant Professor at Cornell
Research Scientist at Google
Research Scientist at Google
Principal Scientist at Google Research
Professor at University of Colorado
Associate Professor at UC Berkeley
Senior Director of Research at Spotify
Professor at Harvard University
Professor at Technion