Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling -- ORSUM 2021

TL;DR

This paper discusses the importance of online, adaptive user modeling and recommendation methods that can handle continuous data streams and dynamic environments in modern online services.

Contribution

It highlights the need for and promotes research on incremental, online algorithms for user modeling and personalization in dynamic data settings.

Findings

Incremental models effectively handle continuous data streams.

Online adaptive methods improve personalization in dynamic environments.

The workshop fosters research on evaluation, privacy, and explainability in online recommender systems.

Abstract

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.