ORSUM 2020 - 3rd Workshop on Online Recommender Systems and User Modeling @ ACM RecSys 2020
ORSUM 2020
- URL: https://orsum.inesctec.pt/orsum2020
- Event Date: 2020-09-25 ~ 2020-09-25
- Submission Date: 2020-07-13
ORSUM 2020 - 3rd Workshop on Online Recommender Systems and User Modeling
ACM RecSys 2020, September 25th, Rio de Janeiro, Brazil
Website:
https://orsum.inesctec.pt/orsum2020
*Important Note*
We are continuously monitoring the COVID-19 situation and considering alternatives to allow for remote participation in both the main RecSys 2020 conference and all its workshops should disruptions still occur in late September.
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Overview
Modern online web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content - e.g. posts, news, products, comments -, but also user feedback - such as ratings, views, reads, clicks, thumbs up -, as well as context information - user device, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to train in offline batches, given the continuous and potentially fast change of content, context and user preferences. Therefore it is important to investigate online methods to be able to transparently adapt to the inherent dynamics of online systems. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated.
The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, as well as other related tasks, such as evaluation, reproducibility, privacy, and explainability.
Relevant topics include, but are not limited to:
- Online user modeling over multidimensional data streams
- Incremental algorithms for recommender systems
- User preference change detection and adaptation
- Context change detection and online adaptation
- Cold-start in incremental recommender systems
- Session-based incremental recommender systems
- Long-term incremental user modeling
- Incremental learning with user-in-the-loop
- Privacy-preserving online user modeling and recommendation
- Online explainability
- Online learning from dynamic knowledge bases
- Online learning from multimedia content
- Online learning from social and news media
- Incremental web and text mining for personalization
- Incremental item ranking models
- Multi-armed bandit algorithms for recommendation
- Time-sensitive online learning
- Automatic online forgetting
- Self-tuning algorithms
- Architectures for continuous user feedback data processing
- Online algorithm evaluation and comparison
- Reproducibility in online methods
- Scalability issues of online algorithms
#################
Submissions
We welcome original, unpublished work in the form of either long and short paper submissions via EasyChair at:
https://easychair.org/conferences/?conf=orsum2020.
Long papers must not exceed 16 pages (excluding references) and should report research at a mature stage.
We also welcome the submission of preliminary results of ongoing research in the form of short papers with a maximum length of 8 pages (excluding references).
Papers must be formatted in LaTeX and follow the template available at the workshop website.
The review process is double-blind, so authors are required to remove any content that allows author identification.
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Important dates
2020-07-13: Paper submission deadline
2020-08-06: Paper acceptance notification
2020-09-03: Camera-ready paper deadline
2020-09-25: Workshop date
All deadlines are at 11:59 pm AoE.
#################
Publication
The proceedings will be published as a dedicated volume in an open repository, such as Proceedings of Machine Learning Research (PMLR) or CEUR-WS.
#################
Organization
João Vinagre
University of Porto and LIAAD - INESC TEC, Portugal
Alípio Mário Jorge
University of Porto and LIAAD - INESC TEC, Portugal
Marie Al-Ghossein
LTCI, Télécom ParisTech, France
Albert Bifet
LTCI, Télécom ParisTech, France
#################
Contact
E-mail: orsum@googlegroups.com
Twitter: https://twitter.com/orsum_ws
ACM RecSys 2020, September 25th, Rio de Janeiro, Brazil
Website:
https://orsum.inesctec.pt/orsum2020
*Important Note*
We are continuously monitoring the COVID-19 situation and considering alternatives to allow for remote participation in both the main RecSys 2020 conference and all its workshops should disruptions still occur in late September.
#################
Overview
Modern online web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content - e.g. posts, news, products, comments -, but also user feedback - such as ratings, views, reads, clicks, thumbs up -, as well as context information - user device, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to train in offline batches, given the continuous and potentially fast change of content, context and user preferences. Therefore it is important to investigate online methods to be able to transparently adapt to the inherent dynamics of online systems. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated.
The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, as well as other related tasks, such as evaluation, reproducibility, privacy, and explainability.
Relevant topics include, but are not limited to:
- Online user modeling over multidimensional data streams
- Incremental algorithms for recommender systems
- User preference change detection and adaptation
- Context change detection and online adaptation
- Cold-start in incremental recommender systems
- Session-based incremental recommender systems
- Long-term incremental user modeling
- Incremental learning with user-in-the-loop
- Privacy-preserving online user modeling and recommendation
- Online explainability
- Online learning from dynamic knowledge bases
- Online learning from multimedia content
- Online learning from social and news media
- Incremental web and text mining for personalization
- Incremental item ranking models
- Multi-armed bandit algorithms for recommendation
- Time-sensitive online learning
- Automatic online forgetting
- Self-tuning algorithms
- Architectures for continuous user feedback data processing
- Online algorithm evaluation and comparison
- Reproducibility in online methods
- Scalability issues of online algorithms
#################
Submissions
We welcome original, unpublished work in the form of either long and short paper submissions via EasyChair at:
https://easychair.org/conferences/?conf=orsum2020.
Long papers must not exceed 16 pages (excluding references) and should report research at a mature stage.
We also welcome the submission of preliminary results of ongoing research in the form of short papers with a maximum length of 8 pages (excluding references).
Papers must be formatted in LaTeX and follow the template available at the workshop website.
The review process is double-blind, so authors are required to remove any content that allows author identification.
#################
Important dates
2020-07-13: Paper submission deadline
2020-08-06: Paper acceptance notification
2020-09-03: Camera-ready paper deadline
2020-09-25: Workshop date
All deadlines are at 11:59 pm AoE.
#################
Publication
The proceedings will be published as a dedicated volume in an open repository, such as Proceedings of Machine Learning Research (PMLR) or CEUR-WS.
#################
Organization
João Vinagre
University of Porto and LIAAD - INESC TEC, Portugal
Alípio Mário Jorge
University of Porto and LIAAD - INESC TEC, Portugal
Marie Al-Ghossein
LTCI, Télécom ParisTech, France
Albert Bifet
LTCI, Télécom ParisTech, France
#################
Contact
E-mail: orsum@googlegroups.com
Twitter: https://twitter.com/orsum_ws
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