Recommender Systems: A Healthy Obsession
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.
Cite
Text
Smyth. "Recommender Systems: A Healthy Obsession." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019790Markdown
[Smyth. "Recommender Systems: A Healthy Obsession." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/smyth2019aaai-recommender/) doi:10.1609/AAAI.V33I01.33019790BibTeX
@inproceedings{smyth2019aaai-recommender,
title = {{Recommender Systems: A Healthy Obsession}},
author = {Smyth, Barry},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9790-9794},
doi = {10.1609/AAAI.V33I01.33019790},
url = {https://mlanthology.org/aaai/2019/smyth2019aaai-recommender/}
}