Call for Papers
Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content - e.g. posts, news, products, comments -, but also user feedback - e.g. ratings, views, reads, clicks -, together with context data. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and unpredictable rate of change of content, context and user preferences or intents, especially in long-term modeling. Therefore, it is important to investigate online methods able to transparently and robustly adapt to the multitude of dynamics of online services.
Incremental models and online learning methods are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. 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 short- and long-term user modeling, recommendation and personalization, and their evaluation regarding multiple dimensions, such as fairness, privacy, explainability, and reproducibility.
Relevant topics include, but are not limited to:
- Stream-based and incremental algorithms
- Continual learning and forgetting
- Lifelong user modeling and recommendation
- User preference change detection and adaptation
- Context change detection and adaptation
- Session-based and sequential learning
- Online distributed and decentralized models
- Online learning with bandits and reinforcement learning
- Online learning from evolving graphs
- Online automated ML
- Online counterfactual learning
- Time-sensitive recommendation
- Privacy and user sovereignty in incremental models
- Interpretability of evolving models
- Online evaluation and benchmarking
- Bias evolution monitoring
- Reproducibility in online methods
- Scalability of online algorithms
- Platforms, software, data, and architectures
- Industrial case studies
ORSUM 2022 : 5th Workshop on Online Recommender Systems and User Modeling (ACM RecSys 2022) will take place in Seattle, WA, USA. It’s a 6 days event starting on Sep 18, 2022 (Sunday) and will be winded up on Sep 23, 2022 (Friday).
ORSUM 2022 falls under the following areas: RECOMMENDER SYSTEMS, ARTIFICIAL INTELLIGENCE, DATA SCIENCE, MACHINE LEARNING, etc. Submissions for this Workshop can be made by Jul 7, 2022. Authors can expect the result of submission by Aug 25, 2022. Upon acceptance, authors should submit the final version of the manuscript on or before Sep 9, 2022 to the official website of the Workshop.
Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.
Other Details of the ORSUM 2022
Credits and Sources
| ORSUM 2022 : 5th Workshop on Online Recommender Systems and User Modeling (ACM RecSys 2022)|