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Gentle reminder: Proposals for presentations (in the form of extended abstracts) for the ACM SIGIR 2026 Workshop on Justice, Emancipation, Democracy, and Information Access (JEDI) are due this Thursday, April 30.
More info: https://jedi.inertial.science/sigir2026/
Please spread the word. 📣 #JEDIWorkshop #SIGIR2026
RE: https://mastodon.acm.org/@JEDI/116437370654701642
Friends of AltRecSys may also be interested in the JEDI workshop at #SIGIR2026 — check out the CFP, extended abstracts due Apr. 30.
The AltRecSys workshop will be held on Friday, October 18, 14:15-17:45 in Room L at the Politecnico di Bari. 14:15-14:25: Chairs’ Welcome 14:25-15:00 Highlight Talk: The Shift from Userist to Post-Userist Recommendation in the Age of Generative AI - Robin Burke 15:00-15:45 Discussion 15:45-16:15 Break 16:15 -16:30 Lightning talks Kim Falk & Robin Verachtert, Binary Vikings: Let’s Talk about Sex, Mr ChatBot Lucas Vinh Tran & Jay Katukuri, JPMorganChase: A Centralized Configuration Driven Modeling Framework for Personalization in Banking and Finance Bart P.
"From Clicks to Carbon: The Environmental Toll of #RecSys" preprint now available at https://arxiv.org/abs/2408.08203
@Recsys #recsys2024
#greenml #greenai #greenrecsys #sustainableai #sustainableml #sustainablerecsys
As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address their environmental impact. In this study, we estimate the environmental impact of recommender systems research by reproducing typical experimental pipelines. Our analysis spans 79 full papers from the 2013 and 2023 ACM RecSys conferences, comparing traditional "good old-fashioned AI" algorithms with modern deep learning algorithms. We designed and reproduced representative experimental pipelines for both years, measuring energy consumption with a hardware energy meter and converting it to CO2 equivalents. Our results show that papers using deep learning algorithms emit approximately 42 times more CO2 equivalents than papers using traditional methods. On average, a single deep learning-based paper generates 3,297 kilograms of CO2 equivalents - more than the carbon emissions of one person flying from New York City to Melbourne or the amount of CO2 one tree sequesters over 300 years.
AltRecSys Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation The AltRecSys workshop provides a venue to discuss interesting, preliminary, offbeat, unexpected, and critical ideas in recommender systems that do not (yet) fit well into the kinds of publications and formats for the main conference or traditional workshops.
Hello #RecSys2024! The Call for Opinions for RecSys is now online: https://altrecsys.github.io/call/
Submissions due Aug. 30. These submissions are intended to seed the conversation, and we are deliberately broad in both topic and format. Submit your weird and provocative ideas!
AltRecSys Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation The AltRecSys workshop provides a venue to discuss interesting, preliminary, offbeat, unexpected, and critical ideas in recommender systems that do not (yet) fit well into the kinds of publications and formats for the main conference or traditional workshops.
JEDI Workshop
Alan Said
Michael Ekstrand