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Communication Dans Un Congrès Année : 2022

ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling

Résumé

Modern online systems for user modeling and recommendation need to continuously deal with complex data streams generated by users at very fast rates. This can be overwhelming for systems and algorithms designed to train recommendation models in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably for long periods of time. Online models that continuously learn from such flows of data 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. 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, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy, fairness and transparency.
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Dates et versions

hal-04468406 , version 1 (20-02-2024)

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João Vinagre, Marie Al-Ghossein, Alípio Mário Jorge, Albert Bifet, Ladislav Peska. ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling. RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022, Sep 2022, Seattle (USA), United States. pp.661--662, ⟨10.1145/3523227.3547411⟩. ⟨hal-04468406⟩
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