Difference between revisions of "RecSys 2019"

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|Paper deadline=2019/04/23
 
|Paper deadline=2019/04/23
 
|Camera ready=2019/07/22
 
|Camera ready=2019/07/22
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|has general chair=Toine Bogers, Alain Said
 +
|has program chair=Domonkos Tikk, Peter Brusilovsky
 +
|Submitted papers=354
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|Accepted papers=76
 
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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

Latest revision as of 06:08, 18 May 2020

RecSys 2019
13th ACM Conference on Recommender Systems
Event in series RecSys
Dates 2019/09/16 (iCal) - 2019/09/20
Homepage: https://recsys.acm.org/recsys19/
Location
Location: Copenhagen, Denmark
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Important dates
Abstracts: 2019/04/15
Papers: 2019/04/23
Submissions: 2019/04/23
Camera ready due: 2019/07/22
Papers: Submitted 354 / Accepted 76 (21.5 %)
Committees
General chairs: Toine Bogers, Alain Said
PC chairs: Domonkos Tikk, Peter Brusilovsky
Table of Contents


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