Difference between revisions of "RecSys 2018"

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|has program chair=Xavier Amatriain, John O’Donovan
 
|has program chair=Xavier Amatriain, John O’Donovan
 
|Acronym          =RecSys 2018
 
|Acronym          =RecSys 2018
|End date        =2018/10/07
+
|End date        =2018-10-07
 
|Series          =RecSys
 
|Series          =RecSys
 
|Type            =Conference
 
|Type            =Conference
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|State            =CA/BC
 
|State            =CA/BC
 
|City            =CA/BC/Vancouver
 
|City            =CA/BC/Vancouver
 +
|Year            =2018
 
|Homepage        =https://recsys.acm.org/recsys18/
 
|Homepage        =https://recsys.acm.org/recsys18/
|Start date      =2018/10/02
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|Start date      =2018-10-02
 
|Title            =12th ACM Conference on Recommender Systems
 
|Title            =12th ACM Conference on Recommender Systems
 
|Accepted papers  =81
 
|Accepted papers  =81
|Submitted papers =331}}
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|Submitted papers =331
 +
}}
 
Topics of interest for RecSys 2018 include (but are not limited to):
 
Topics of interest for RecSys 2018 include (but are not limited to):
  

Latest revision as of 04:10, 6 December 2021


Event Rating

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List of all ratings can be found at RecSys 2018/rating

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: CA/BC/Vancouver, CA/BC, CA
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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