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Second Workshop on Context Representation in User Modelling

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Abstract

The second edition of the Context Representation in User Modelling (CRUM) workshop, themed “Human-Centred Context”, aimed to provide a venue for research on contextual information within the evolving landscape of UMAP. This workshop explored context through a human-centred lens, emphasising the nuanced interplay between subjective and objective contexts in user experience rather than traditional domain-centred approaches. This theme encouraged accounting for the interaction between the user model and the contextual environment. The workshop also fostered discussion on the landscape of contextual representation – ranging from physical settings and social dynamics to technological interfaces – as well as on interaction design, user experiences, and influencing user behaviour. Prospective authors were invited to submit two types of papers: full papers of up to seven pages and two-page provocation papers. Accepted submissions are summarised in this workshop report.
Original languageEnglish
Title of host publicationUMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery
Pages225-228
Number of pages4
ISBN (Electronic)9798400704666
DOIs
Publication statusPublished - 28 Jun 2024

Publication series

NameUMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

Bibliographical note

Publisher Copyright:
© 2024 Owner/Author.

Funding

This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6223 and with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224. This research was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at Trinity College Dublin. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology, is funded by Science Foundation Ireland through the SFI Research Centres Programme. This research was partially funded with the financial support of the Dutch Railways. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

FundersFunder number
SFI
Trinity College Dublin
Science Foundation Ireland18/CRT/6223, 13/RC/2106_P2, 18/CRT/6224

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