Welcome to RS_c, the central platform for the RecSys community. We provide curated lists of recommender-systems datasets, algorithms, books, conferences and many resources more. Maybe most importantly, we publish the latest recommender-system news. If you want your news to be reported on RS_c, read here.
2023 ACM RecSys Conference with the lowest number of industry sponsors since 2015
November 10, 2023
The 17th ACM Conference on Recommender Systems (ACM RecSys) in Singapore has ended some weeks ago. Like every year, there were many amazing presentations, and many well-known IT companies sponsored the conference, including Google, Meta, Amazon, and Netflix. However, the number of sponsors of the ACM RecSys conference took a sharp fall this year.
Only 10 companies sponsored the 2023 ACM Conference on Recommender Systems (see picture below). This is the lowest number since 2014 and 2015. In the previous years, between 2018 and 2022, the number of sponsors seemed stable at around 20 per year (with an exception in the year of the COVID-19 pandemic, which had 11 sponsors for the online-only conference).
So what is the reason? The declining interest of IT companies in recommender systems? A generally tough financial situation, that forces many companies to think twice before sponsoring an academic conference? The location of ACM RecSys 2023? …? What do you think?
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries.
We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).