Workshop on Online Recommender Systems and User Modeling

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. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and unpredictable rate of change of content, context and user preferences or intents, especially in long-term modeling. Therefore, it is important to investigate online methods able to transparently and robustly adapt to the multitude of dynamics of online services.
Incremental models and online learning methods 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. 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 short- and long-term user modeling, recommendation and personalization, and their evaluation regarding multiple dimensions, such as fairness, privacy, explainability, and reproducibility.

  • João Vinagre / INESC TEC and University of Porto, Portugal
  • Marie Al-Ghossein / Crossing Minds, France
  • Alípio Jorge / INESC TEC and University of Porto, Portugal
  • Albert Bifet / University of Waikato, New Zealand and LTCI – Télécom ParisTech, France
  • Ladislav Peška / Charles University, Czechia


Full day.

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