Difference between revisions of "RecSys 2017"

From Openresearch
Jump to: navigation, search
 
(2 intermediate revisions by the same user not shown)
Line 13: Line 13:
 
|Paper deadline=2017/04/03
 
|Paper deadline=2017/04/03
 
|Camera ready=2017/07/07
 
|Camera ready=2017/07/07
|has general chair=Sole Pera, Michael Ekstrand
+
|has general chair=Paolo Cremonesi, Francesco Ricci
 
|has program chair=Alexander Tuzhilin, Shlomo Berkovsky
 
|has program chair=Alexander Tuzhilin, Shlomo Berkovsky
|Submitted papers=331
+
|Submitted papers=125
|Accepted papers=81
+
|Accepted papers=26
|Accepted short papers=49
+
|Accepted short papers=20
 
}}
 
}}
 +
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

Latest revision as of 16:35, 21 April 2020

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: Como, Italy
Loading map...

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