Difference between revisions of "RecSys 2019"
		
		
		
		
		
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{{Event  | {{Event  | ||
| + | |Acronym=RecSys 2019  | ||
| + | |Title=13th ACM Conference on Recommender Systems  | ||
| + | |Series=RecSys  | ||
| + | |Type=Conference  | ||
| + | |Start date=2019/09/16  | ||
| + | |End date=2019/09/20  | ||
|Submission deadline=2019/04/23  | |Submission deadline=2019/04/23  | ||
| + | |Homepage=https://recsys.acm.org/recsys19/  | ||
| + | |City=Copenhagen  | ||
| + | |Country=Denmark  | ||
|Abstract deadline=2019/04/15  | |Abstract deadline=2019/04/15  | ||
|Paper deadline=2019/04/23  | |Paper deadline=2019/04/23  | ||
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|has general chair=Toine Bogers, Alain Said  | |has general chair=Toine Bogers, Alain Said  | ||
|has program chair=Domonkos Tikk, Peter Brusilovsky  | |has program chair=Domonkos Tikk, Peter Brusilovsky  | ||
| − | |  | + | |Submitted papers=354  | 
| − | |  | + | |Accepted papers=76  | 
| − | + | |State=US/NY}}  | |
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Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered  | Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered  | ||
Revision as of 23:37, 24 October 2021
Event Rating
| median | worst | 
|---|---|
List of all ratings can be found at RecSys 2019/rating
| 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, US/NY, Denmark | 
| 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