Difference between revisions of "RecSys 2019"
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|has program chair=Domonkos Tikk, Peter Brusilovsky | |has program chair=Domonkos Tikk, Peter Brusilovsky | ||
|Acronym =RecSys 2019 | |Acronym =RecSys 2019 | ||
− | |End date =2019 | + | |End date =2019-09-20 |
|Series =RecSys | |Series =RecSys | ||
|Type =Conference | |Type =Conference | ||
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|State =US/NY | |State =US/NY | ||
|City =US/NY/Copenhagen | |City =US/NY/Copenhagen | ||
+ | |Year =2019 | ||
|Homepage =https://recsys.acm.org/recsys19/ | |Homepage =https://recsys.acm.org/recsys19/ | ||
− | |Start date =2019 | + | |Start date =2019-09-16 |
|Title =13th ACM Conference on Recommender Systems | |Title =13th ACM Conference on Recommender Systems | ||
|Accepted papers =76 | |Accepted papers =76 | ||
− | |Submitted papers =354}} | + | |Submitted papers =354 |
+ | }} | ||
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 | ||
Latest revision as of 04:14, 6 December 2021
Event Rating
median | worst |
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List of all ratings can be found at RecSys 2019/rating
RecSys 2019 | |
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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: | US/NY/Copenhagen, US/NY, US |
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