Important note: Due to concerns about COVID-19, RecSys 2020 will be a fully virtual conference.
ORSUM will coordinate closely with
RecSys organizers to ensure a suitable format and calendar for authors and
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
We welcome original, unpublished work in the form of either long or short paper submissions via EasyChair at:
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 workshop template (available
The review process is double-blind, so authors are required to remove any content that allows author identification.
2020-07-29: Paper submission deadline
2020-09-08: Paper acceptance notification
2020-09-22: Camera-ready paper deadline
2020-09-25: Workshop date
All deadlines are at 11:59pm AoE.
The proceedings will be available as a dedicated volume in an open repository.