Smileska, Cvetanka and Koceska, Natasa and Koceski, Saso and Trajkovik, Vladimir (2019) Development and Evaluation of Methodology for Personal Recommendations Applicable in Connected Health. In: Enhanced Living Environments. Springer, pp. 80-95.
Full text not available from this repository.Abstract
In this paper, a personal recommendation system of outdoor physical activities using solely user’s history data and without application of collaborative filtering algorithms is proposed and evaluated. The methodology proposed contains four phases: data fuzzyfication, activity usefulness calculation, estimation of most useful activities, activities classification. In the process of classification several data mining techniques were compared such as: decision trees algorithms, decision rules algorithm, Bayes algorithm and support vector machines. The pro-posed algorithm has been experimentally validated using real dataset collected in a certain period of time from a community of 1000 active users. Recommendations generated by the system were related to weight loss. The results show that our generated recommendations have high accuracy, up to 95%.
| Item Type: | Book Section |
|---|---|
| Subjects: | Natural sciences > Computer and information sciences Medical and Health Sciences > Health biotechnology |
| Divisions: | Faculty of Computer Science |
| Depositing User: | Natasa Koceska |
| Date Deposited: | 01 Feb 2019 10:13 |
| Last Modified: | 01 Feb 2019 10:13 |
| URI: | https://eprints.ugd.edu.mk/id/eprint/21263 |
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