Difference between revisions of "RecSys 2019"
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|Camera ready=2019/07/22 | |Camera ready=2019/07/22 | ||
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| + | 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 | ||
Revision as of 15:38, 21 April 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 |
| 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