Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling — ORSUM 2021. (arXiv:2201.05156v1 [cs.IR])

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 — user device, spatial or temporal data, user task
or activity, weather. This can be overwhelming for systems and algorithms
designed to train in batches, given the continuous and potentially fast change
of content, context and user preferences or intents. Therefore, it is important
to investigate online methods able to transparently adapt to the inherent
dynamics of online services. Incremental models that learn from data streams
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. 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 and explainability.

Source: https://arxiv.org/abs/2201.05156

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