ACM RecSys 2025 to be held in Prague
The next edition of the ACM RecSys Conference will take place in Prague, Czech Republic, from September 22 to 26, 2025. ACM RecSys, an A-ranked conference, continues to attract top-tier research in recommendation algorithms, evaluation metrics, and applications in various domains.
Prague, a well-established academic hub with institutions excelling in computer science and AI, offers an ideal backdrop for fostering academic and industry collaboration on recommender systems. The city is easily accessible and balances historical and modern venues, ensuring an engaging experience beyond the technical sessions.
For 2025, we can expect increased focus on trending topics such as fairness in recommendations, hybrid systems combining LLMs and traditional models, and energy-efficient algorithms. Workshops and tutorials will likely cover advancements in reinforcement learning, multi-objective recommendation, and scalable systems for large datasets. With growing attention on green AI, sessions addressing the trade-offs between computational cost and recommendation quality may play a prominent role.
Mark the dates for RecSys 2025 in Prague — this promises to be a must-attend event for recommender systems researchers and practitioners aiming to stay at the forefront of the field.
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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).