Call for Papers

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