Is SIGIR 2025 desk-rejecting RecSys manuscripts only to reverse its decision later?
On X (Twitter), some researchers report that SIGIR 2025 had desk-rejected a large proportion of submissions (35%), most of them being related to recommender systems. Xiangnan He even concluded that recommender systems — mentioned in the call for papers and historically one of the most popular topics — were “kicked off from ACM SIGIR” entirely.
Suzan Verberne, program chair of SIGIR 2025, clarified that recommender systems were not “kicked off” SIGIR, and of the papers sent to reviewers, the majority, “by far”, was about recommender systems.
She continued that SIGIR looked at every paper to decide whether it was a good fit for SIGIR or if it would be a better fit for other venues such as KDD.
How exactly that decision was made, remains, so far, unclear. It also appears strange that this decision was made after authors submitted their manuscripts and not explained in detail beforehand in the call for papers. Eventually, the desk rejected authors spend likely a lot of time to prepare their manuscripts for SIGIR.
Probably even more strange, it appears that SIGIR since then reversed it’s decision. On X, some report that their desk-rejected papers are actually now back for review.
Who knows the full story? Let us know in the comments!
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About The Author
Joeran Beel
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).