Difference between revisions of "Event:RecSys 2019"

From ConfIDent
(mobo import Concept___Events-migrated)
(mobo import Concept___Events-migrated)
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|Title=13th ACM Conference on Recommender Systems
 
|Title=13th ACM Conference on Recommender Systems
 
|Type=Conference
 
|Type=Conference
|Submission deadline=2019/04/23
 
 
|Homepage=https://recsys.acm.org/recsys19/
 
|Homepage=https://recsys.acm.org/recsys19/
 
|City=Copenhagen
 
|City=Copenhagen
 
|Country=Country:DK
 
|Country=Country:DK
|Abstract deadline=2019/04/15
 
|Paper deadline=2019/04/23
 
|Camera ready=2019/07/22
 
 
|has general chair=Toine Bogers, Alain User:Curator 84
 
|has general chair=Toine Bogers, Alain User:Curator 84
 
|has program chair=Domonkos Tikk, Peter Brusilovsky
 
|has program chair=Domonkos Tikk, Peter Brusilovsky
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|Event Mode=on site
 
|Event Mode=on site
 
}}
 
}}
 +
{{Event Deadline
 +
|Abstract Deadline=2019/04/15
 +
|Paper Deadline=2019/04/23
 +
|Camera-Ready Deadline=2019/07/22
 +
|Submission Deadline=2019/04/23
 +
}}
 +
{{S Event}}
 
Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered
 
Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered
  

Revision as of 20:52, 22 September 2022

Deadlines
2019-04-15
2019-04-23
2019-07-22
2019-04-23
15
Apr
2019
Abstract
23
Apr
2019
Submission
23
Apr
2019
Paper
22
Jul
2019
Camera-Ready
Venue

Copenhagen, Denmark

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Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered

  • Algorithm scalability, performance, and implementations
  • Bias, bubbles and ethics of recommender systems
  • Case studies of real-world implementations
  • Context-aware recommender systems
  • Conversational recommender systems
  • Cross-domain recommendation
  • Economic models and consequences of recommender systems
  • Evaluation metrics and studies
  • Explanations and evidence
  • Innovative/New applications
  • Interfaces for recommender systems
  • Novel machine learning approaches to recommendation algorithms (deep learning, reinforcement learning, etc.)
  • Preference elicitation
  • Privacy and Security
  • Social recommenders
  • User modelling
  • Voice, VR, and other novel interaction paradigms
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