Difference between revisions of "RecSys 2017"
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|Start date=2017/08/27 | |Start date=2017/08/27 | ||
|End date=2017/08/31 | |End date=2017/08/31 | ||
| − | |Submission deadline= | + | |Submission deadline=2017/04/03 |
|Homepage=https://recsys.acm.org/recsys17/ | |Homepage=https://recsys.acm.org/recsys17/ | ||
|City=Como | |City=Como | ||
|Country=Italy | |Country=Italy | ||
| − | |Abstract deadline= | + | |Abstract deadline=2017/03/27 |
| − | |Paper deadline= | + | |Paper deadline=2017/04/03 |
| − | |Camera ready= | + | |Camera ready=2017/07/07 |
| − | |has general chair= | + | |has general chair=Paolo Cremonesi, Francesco Ricci |
| − | |has program chair= | + | |has program chair=Alexander Tuzhilin, Shlomo Berkovsky |
| − | |Submitted papers= | + | |Submitted papers=125 |
| − | |Accepted papers= | + | |Accepted papers=26 |
| − | |Accepted short papers= | + | |Accepted short papers=20 |
}} | }} | ||
| + | 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 | ||
Latest revision as of 15:35, 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 |
Loading map... | |
| Important dates | |
| Abstracts: | 2017/03/27 |
| Papers: | 2017/04/03 |
| Submissions: | 2017/04/03 |
| Camera ready due: | 2017/07/07 |
| Accepted short papers: | 20 |
| Papers: | Submitted 125 / Accepted 26 (20.8 %) |
| 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
Facts about "RecSys 2017"
| Abstract deadline | March 27, 2017 + |
| Acceptance rate | 20.8 + |
| Accepted papers | 26 + |
| Accepted short papers | 20 + |
| Acronym | RecSys 2017 + |
| Camera ready due | July 7, 2017 + |
| End date | August 31, 2017 + |
| Event in series | RecSys + |
| Event type | Conference + |
| Has coordinates | 45° 56' 22", 9° 8' 58"Latitude: 45.939475 Longitude: 9.1494111111111 + |
| Has general chair | Paolo Cremonesi + and Francesco Ricci + |
| Has location city | Como + |
| Has location country | Category:Italy + |
| Has program chair | Alexander Tuzhilin + and Shlomo Berkovsky + |
| Homepage | https://recsys.acm.org/recsys17/ + |
| IsA | Event + |
| Paper deadline | April 3, 2017 + |
| Start date | August 27, 2017 + |
| Submission deadline | April 3, 2017 + |
| Submitted papers | 125 + |
| Title | 11th ACM Conference on Recommender Systems + |