A Novel Recommender System for helping Marathoners to Achieve a new Personal-Best
|Title:||A Novel Recommender System for helping Marathoners to Achieve a new Personal-Best||Authors:||Smyth, Barry
|Permanent link:||http://hdl.handle.net/10197/9053||Date:||31-Aug-2017||Abstract:||We describe a novel application for recommender systems -- helping marathon runners to run a new personal-best race-time -- by predicting a challenging, but achievable target-time, and by recommending a tailored race-plan to achieve this time. A comprehensive evaluation of prediction accuracy and race-plan quality is provided using a large-scale dataset with almost 400,000 runners from the last 12 years of the Chicago marathon.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||ACM||Copyright (published version):||2017 ACM||Keywords:||Recommender Systems||DOI:||10.1145/3109859.3109874||Language:||en||Status of Item:||Peer reviewed||Is part of:||RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems||Conference Details:||RecSys '17 the Eleventh ACM Conference on Recommender Systems, Como, Italy, 27-31 August 2017|
|Appears in Collections:||Insight Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.