Development and Evaluation of Methodology for Personal Recommendations Applicable in Connected Health.

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.
Official URL: https://link.springer.com/book/10.1007%2F978-3-030...

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: http://eprints.ugd.edu.mk/id/eprint/21263

Actions (login required)

View Item View Item