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Running Recommendations: Personalisation Opportunities for Health and Fitness

Publication Type: 
Refereed Conference Meeting Proceeding
The history of user modelling, personalisation and recommender systems is, in large part, a web-tale: a story of sites and services learning about users, in order to provide more tailored online experiences. The rapid rise of mobile computing, combined with wearable sensors, 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. In our increasingly digitized world almost everything we do creates a record that is stored somewhere, whether we are purchasing a book, calling a friend, or watching a movie. But in the connected world of the internet of things (IoT) this no longer limited to our online activities: whether we are exercising in the park, shopping for groceries, or even falling asleep, data continues to be created and captured by a variety of apps and service providers. All of this introduces exciting new application opportunities for user modelling, personalisation, and recommendation, by providing new types of data, and new reasons to harness these data in a host of novel contexts. Of course it also amplies some critical challenges, especially when it comes to personal data and privacy. This keynote will consider some of these challenges and opportunities, with a particular emphasis on health and exercise. One example of a open opportunity is embodied in the many and varied ways that people are using data-driven apps to track and share their exercise. This is just the beginning of a new generation of intelligent assistants capable of harnessing personalisation technologies to better support users as they strive to get the most from their exercise, and help them to live healthier more active lifestyles. As a concrete case-study this keynote will draw on recent work to support recreational runners as they train for, plan, and run marathon races
Conference Name: 
UMAP’18, July 8–11, 2018, Singapore
Digital Object Identifer (DOI): 
Publication Date: 
Research Group: 
National University of Ireland, Dublin (UCD)
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