Call for Papers (CfP) of the 16th ACM Conference on Recommender Systems (ACM RecSys 2022)
The ACM RecSys organizers just released the call for papers for the 16th ACM Conference on Recommender Systems (RecSys 2022), which will take place from September 18–23, 2022 in Seattle, WA, USA.
IMPORTANT DATES
- Abstract submission deadline: April 26th, 2022
- Paper submission deadline: May 3rd, 2022
- Author notification: June 28th, 2022
- Camera-ready version deadline: July 26th, 2022
Topics of interest
- Algorithm scalability, performance, and implementations
- Bias, fairness, bubbles, and ethics of recommender systems
- Case studies of real-world implementations
- Conversational and natural language recommender systems
- Cross-domain recommendation
- Data characteristics and processing challenges underlying recommender systems
- Economic models and consequences of recommender systems
- Interfaces for recommender systems
- Multi-stakeholder recommendations
- New aspects of recommender systems evaluation
- Novel approaches to recommendation, including voice, VR/AR, etc.
- Preference elicitation
- Privacy and security
- Socially- and context-aware recommender systems
- Systems challenges such as scalability, data quality, and performance
- User studies of recommendation applications
The Web Conference 2023 requires at least one full non-student registration for accepted papers, even for students.
More than a decade of the ACM RecSys Conference [Alan Said, 2017]
The ACM RecSys Conference introduces major changes to the submission process (no short-papers; response phase; ethics)
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).