RecSys 2017: Difference between revisions

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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

Revision as of 14:33, 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
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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: 49
Papers: Submitted 331 / Accepted 81 (24.5 %)
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