ECIR 2025 CfP published
The 47th European Conference on Information Retrieval (ECIR 2025) will take place from April 6 to April 10, 2025, in Lucca, Italy. ECIR is a premier European forum for the presentation of research in Information Retrieval (IR), covering areas like search engines, recommendation systems, and data retrieval.
Several paper tracks are available:
- Full Papers (due October 16, 2024): 12-page papers on topics like ranking models, recommender systems, and explainability in AI, peer-reviewed through a double-blind process.
- Short Papers (due October 30, 2024): These 6-page submissions focus on emerging ideas and trends in IR.
- Workshops: Proposals should cover specialized IR topics with an interactive format.
- Demo Papers (due October 30, 2024): Practical applications of IR research can be showcased through demos.
- Doctoral Consortium Papers (due November 10, 2024): PhD students can present ongoing research and get feedback from senior scholars.
- Industry Day Presentations (due November 13, 2024): Focuses on the integration of IR systems with generative AI, fostering industry-academia collaboration.
- Reproducibility Papers (due October 30, 2024): Encouraging replication studies to validate existing IR research.
- IR for Good Papers (due November 6, 2024): Papers addressing ethical and societal challenges in IR.
- Tutorial Proposals (due November 8, 2024): Tutorials will offer in-depth sessions on IR topics with hands-on interaction.
CfP: ACM TORS Special Issue on Large Language Models for Recommender Systems (LLM4RecSys)
Call for Papers: Special Issue on Recommender Systems in Industry – Challenges and Solutions (ACM TORS)
CfP: Special Issue on Responsible Recommender Systems (ACM TIST)
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).