RecSys 2019: Difference between revisions
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|Country=Denmark | |Country=Denmark | ||
|Abstract deadline=2019/04/15 | |Abstract deadline=2019/04/15 | ||
|Paper deadline=2019/04/23 | |||
|Camera ready=2019/07/22 | |||
|has general chair=Toine Bogers, Alain Said | |||
|has program chair=Domonkos Tikk, Peter Brusilovsky | |||
|Submitted papers=354 | |||
|Accepted papers=76 | |||
}} | }} | ||
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 | |||
Latest revision as of 05:08, 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 |
| 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