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 -- user device, spacial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams 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. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online.

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, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy and explainability.

Relevant topics include, but are not limited to:

• Incremental algorithms for recommender systems
• User preference change detection and adaptation
• Incremental learning with user-in-the-loop
• Context change detection and adaptation
• Cold-start in incremental recommender systems
• Session-based and sequential learning
• Online learning from dynamic knowledge graphs
• Online learning from multimedia content
• Online learning from social and news media
• Lifelong user modeling and recommendation
• Self-tuning algorithms
• Forgetting and retention mechanisms
• Time-sensitive recommendation
• Privacy issues in online user modeling and recommendation
• Online bandits and reinforcement learning
• Online counterfactual learning and causality
• Explainability in dynamic environments
• Online methods evaluation and comparison
• Reproducibility in online methods
• Platforms and architectures for continuous user feedback processing
• Scalability issues of online algorithms