Workshop on Online Recommender Systems and User Modeling

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 – e.g. ratings, views, reads, clicks, thumbs up -, as well as context information – 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 related tasks, such as evaluation, reproducibility, privacy and explainability.

  • João Vinagre, LIAAD – INESC TEC and FCUP – University of Porto
  • Alípio Mário Jorge, LIAAD – INESC TEC and FCUP – University of Porto
  • Marie Al-Ghossein, LTCI, Télécom ParisTech
  • Albert Bifet, LTCI, Télécom ParisTech


14:0018:00, Attend in Whova

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