Difference between revisions of "RecSys 2018"

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|has general chair=Sole Pera, Michael Ekstrand
 
|has general chair=Sole Pera, Michael Ekstrand
 
|has program chair=Xavier Amatriain, John O’Donovan
 
|has program chair=Xavier Amatriain, John O’Donovan
|Submitted papers=189
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|Submitted papers=331
|Accepted papers=76
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|Accepted papers=81
|Accepted short papers=40
 
 
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Topics of interest for RecSys 2018 include (but are not limited to):
 +
 +
* Conversational recommender systems
 +
*    Novel machine learning approaches to recommendation algorithms
 +
*    Evaluation metrics and studies
 +
*    Explanations and evidence
 +
*    Algorithm scalability, performance, and implementations
 +
*    Innovative/New applications
 +
*    Voice, VR, and other novel interaction paradigms
 +
*    Case studies of real-world implementations
 +
*    Preference elicitation
 +
*    Privacy and Security
 +
*    Economic models and consequences of recommender systems
 +
*    Personalisation
 +
*    Social recommenders
 +
*    User modelling

Latest revision as of 14:53, 22 April 2020

RecSys 2018
12th ACM Conference on Recommender Systems
Event in series RecSys
Dates 2018/10/02 (iCal) - 2018/10/07
Homepage: https://recsys.acm.org/recsys18/
Location
Location: Vancouver, Canada
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Important dates
Abstracts: 2018/04/30
Papers: 2018/05/07
Submissions: 2018/05/07
Camera ready due: 2018/08/06
Papers: Submitted 331 / Accepted 81 (24.5 %)
Committees
General chairs: Sole Pera, Michael Ekstrand
PC chairs: Xavier Amatriain, John O’Donovan
Table of Contents


Topics of interest for RecSys 2018 include (but are not limited to):

  • Conversational recommender systems
  • Novel machine learning approaches to recommendation algorithms
  • Evaluation metrics and studies
  • Explanations and evidence
  • Algorithm scalability, performance, and implementations
  • Innovative/New applications
  • Voice, VR, and other novel interaction paradigms
  • Case studies of real-world implementations
  • Preference elicitation
  • Privacy and Security
  • Economic models and consequences of recommender systems
  • Personalisation
  • Social recommenders
  • User modelling