Call for Papers

Modern 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 - e.g. ratings, views, reads, clicks, thumbs up -, as well as context information - device used, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to work in batch, given the fast rate of change of content, usage patterns and contextual variables. Incremental models that learn from data streams are gaining attention, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization exploits content, user feedback and context in data, and can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated.

The objective of this workshop is to bring together researchers and practitioners interested in incremental and adaptive approaches to recommendation and personalization, as well as other related tasks, such as evaluation, web content mining, context-awareness or architectures.

Relevant topics include, but are not limited to:

• Incremental user modeling
• Incremental recommender systems
• Incremental web mining
• Incremental text mining
• Online learning from user generated data
• Online learning from dynamic web content
• Online learning from multimedia content
• Online learning from social data
• Context-aware online learning
• Time-aware online learning
• Adaptive algorithms and interfaces
• Online evaluation
• Architectures for continuous web data processing
• Explainability in incremental recommenders
• Privacy assurance in incremental recommenders
• Online parameter optimization