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Running Recommendations: Personalisation Opportunities for Health and Fitness

Author(s)
Smyth, Barry  
Uri
http://hdl.handle.net/10197/10124
Date Issued
2018-07-11
Date Available
2019-04-24T11:26:58Z
Abstract
The history of personalisation and recommender systems is, in large part, a web-tale: a story of sites and services that learn about users, in order to provide more tailored experiences. The rapid rise of mobile computing, combined with wearable sensors, and an increasingly connected IoT world, has begun to shift the potential for personalisation, from the virtual world of the web, to the physical world in which we live, work, and play. This talk will consider exciting new application opportunities for user modelling, personalisation, and recommendation in the area of personal health and fitness, with a particular emphasis on how these technologies can help people to exercise more effectively, and by drawing from recent results for marathon runners.
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2018 the Author
Subjects

Recommender systems

Sports analytics

Data analytics

DOI
10.1145/3209219.3209269
Web versions
http://www.um.org/umap2018/
Language
English
Status of Item
Peer reviewed
Journal
UMAP '18 Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
Conference Details
The 26th Conference on User Modeling, Adaption and Personalization (UMAP'18), Nanyang Technological University, Singapore, 8-11 July 2018
ISBN
978-1-4503-5589-6
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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insight_publication.pdf

Size

320.89 KB

Format

Adobe PDF

Checksum (MD5)

1fc5b768539cce5f88fc37707a986438

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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