ORSUM 2018 - Workshop on Online Recommender Systems and User Modeling (The Web Conference 2018)

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
Architectures for continuous web data processing
Adaptive algorithms
Online evaluation
Privacy assurance in incremental recommenders
Online parameter optimization