Workshop on Online Recommender Systems and User Modeling

The ever-growing nature of user generated data in online systems poses obvious challenges on how we process such data. Typically, this issue is regarded as a scalability problem and has been mainly addressed with distributed algorithms able to train on massive amounts of data in short time windows. However, data is inevitably adding up at high speeds. Eventually one needs to discard or archive some of it. Moreover, the dynamic nature of data in user modeling and recommender systems, such as change of user preferences, and the continuous introduction of new users and items make it increasingly difficult to maintain up-to-date, accurate recommendation models.

These challenges motivate the research on incremental, on-line methods that adapt to incoming data without retraining models from scratch. Online learning algorithms and data stream mining have gained maturity in recent years, however they have not been thoroughly studied in recommendation, and need further investigation.

The objective of this workshop is to bring together researchers and practitioners interested in incremental and adaptive approaches to stream-based user modeling, recommendation and personalization, including algorithms, evaluation issues, incremental content and context mining, privacy and transparency, temporal recommendation or software frameworks for continuous learning.

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


Thursday, Sept 19, 2019, 14:00-17:30



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