Difference between revisions of "RecSys 2017"

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|Accepted short papers=20
 
|Accepted short papers=20
 
|Acronym          =RecSys 2017
 
|Acronym          =RecSys 2017
|End date        =2017/08/31
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|End date        =2017-08-31
 
|Series          =RecSys
 
|Series          =RecSys
 
|Type            =Conference
 
|Type            =Conference
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|State            =IT/25
 
|State            =IT/25
 
|City            =IT/25/Como
 
|City            =IT/25/Como
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|Year            =2017
 
|Homepage        =https://recsys.acm.org/recsys17/
 
|Homepage        =https://recsys.acm.org/recsys17/
|Start date      =2017/08/27
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|Start date      =2017-08-27
 
|Title            =11th ACM Conference on Recommender Systems
 
|Title            =11th ACM Conference on Recommender Systems
 
|Accepted papers  =26
 
|Accepted papers  =26
|Submitted papers =125}}
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|Submitted papers =125
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Topics of interest for RecSys 2017 include (but are not limited to):
 
Topics of interest for RecSys 2017 include (but are not limited to):
 
   
 
   

Latest revision as of 03:45, 6 December 2021


Event Rating

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List of all ratings can be found at RecSys 2017/rating

RecSys 2017
11th ACM Conference on Recommender Systems
Event in series RecSys
Dates 2017-08-27 (iCal) - 2017-08-31
Homepage: https://recsys.acm.org/recsys17/
Location
Location: IT/25/Como, IT/25, IT
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Important dates
Abstracts: 2017/03/27
Papers: 2017/04/03
Submissions: 2017/04/03
Camera ready due: 2017/07/07
Accepted short papers: 20
Papers: Submitted 125 / Accepted 26 (20.8 %)
Committees
General chairs: Paolo Cremonesi, Francesco Ricci
PC chairs: Alexander Tuzhilin, Shlomo Berkovsky
Table of Contents

Topics of interest for RecSys 2017 include (but are not limited to):

  • Algorithm scalability
  • Case studies of real-world implementations
  • Conversational recommender systems
  • Context-aware recommenders
  • Evaluation metrics and studies
  • Explanations and evidence
  • Field and user studies
  • Group recommenders
  • Innovative/New applications
  • Machine learning for recommendation
  • Mobile and multi-channel recommendations
  • Novel paradigms
  • Personalisation
  • Preference elicitation
  • Privacy and Security
  • Recommendation algorithms
  • Social recommenders
  • Semantic technologies for recommendation
  • Trust and reputation
  • Theoretical foundations
  • User interaction and interfaces
  • User modelling