Recommender Systems: A Healthy Obsession
|Title:||Recommender Systems: A Healthy Obsession||Authors:||Smyth, Barry||Permanent link:||http://hdl.handle.net/10197/10916||Date:||1-Jun-2018||Online since:||2019-07-15T14:02:05Z||Abstract:||We propose endurance sports as a rich and novel domain for recommender systems and machine learning research. As sports like marathon running, triathlons, and mountain biking become more and more popular among recreational athletes, there exists a growing opportunity to develop solutions to a number of interesting prediction, classification, and recommendation challenges, to better support the complex training and competition needs of athletes. Such solutions have the potential to improve the health and well-being of large populations of users, by promoting and optimising exercise as part of a productive and healthy lifestyle.||Funding Details:||Science Foundation Ireland||metadata.dc.description.othersponsorship:||Insight Research Centre||Type of material:||Journal Article||Copyright (published version):||2019 Association for the Advancement of Artificial Intelligence||Keywords:||Recommender systems; Collaborative filtering; Content-based recommendation; Exercise; Machine learning||DOI:||10.xxx||Other versions:||https://aaai.org/Conferences/AAAI-19/aaai-19-senior-member-presentation-track/||Language:||en||Status of Item:||Peer reviewed||Conference Details:||AAAI-19: 33rd AAAI Conference on Artificial Intelligence, Hilton Hawaiian Village, Honolulu, Hawaii, USA, 27 January - 1 February 2019|
|Appears in Collections:||Insight Research Collection|
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