Difference between revisions of "RecSys 2019"

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*  Algorithm scalability, performance, and implementations
 
*  Algorithm scalability, performance, and implementations
 
*  Bias, bubbles and ethics of recommender systems
 
*  Bias, bubbles and ethics of recommender systems
*     Case studies of real-world implementations
+
* Case studies of real-world implementations
*     Context-aware recommender systems
+
* Context-aware recommender systems
*     Conversational recommender systems
+
* Conversational recommender systems
*     Cross-domain recommendation
+
* Cross-domain recommendation
*     Economic models and consequences of recommender systems
+
* Economic models and consequences of recommender systems
*     Evaluation metrics and studies
+
* Evaluation metrics and studies
*     Explanations and evidence
+
* Explanations and evidence
*     Innovative/New applications
+
* Innovative/New applications
*     Interfaces for recommender systems
+
* Interfaces for recommender systems
*     Novel machine learning approaches to recommendation algorithms (deep learning, reinforcement learning, etc.)
+
* Novel machine learning approaches to recommendation algorithms (deep learning, reinforcement learning, etc.)
*     Preference elicitation
+
* Preference elicitation
*     Privacy and Security
+
* Privacy and Security
*     Social recommenders
+
* Social recommenders
*     User modelling
+
* User modelling
*     Voice, VR, and other novel interaction paradigms
+
* Voice, VR, and other novel interaction paradigms

Latest revision as of 06:08, 18 May 2020

RecSys 2019
13th ACM Conference on Recommender Systems
Event in series RecSys
Dates 2019/09/16 (iCal) - 2019/09/20
Homepage: https://recsys.acm.org/recsys19/
Location
Location: Copenhagen, Denmark
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Important dates
Abstracts: 2019/04/15
Papers: 2019/04/23
Submissions: 2019/04/23
Camera ready due: 2019/07/22
Papers: Submitted 354 / Accepted 76 (21.5 %)
Committees
General chairs: Toine Bogers, Alain Said
PC chairs: Domonkos Tikk, Peter Brusilovsky
Table of Contents


Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered

  • Algorithm scalability, performance, and implementations
  • Bias, bubbles and ethics of recommender systems
  • Case studies of real-world implementations
  • Context-aware recommender systems
  • Conversational recommender systems
  • Cross-domain recommendation
  • Economic models and consequences of recommender systems
  • Evaluation metrics and studies
  • Explanations and evidence
  • Innovative/New applications
  • Interfaces for recommender systems
  • Novel machine learning approaches to recommendation algorithms (deep learning, reinforcement learning, etc.)
  • Preference elicitation
  • Privacy and Security
  • Social recommenders
  • User modelling
  • Voice, VR, and other novel interaction paradigms