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 |
| 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