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Social health recommender system

Social health recommender system The amount of information and services available on the web represents an important opportunity for people to enrich and share knowledges. The easy access and the continuous growth of data in the web are responsible of an overload of information that leads the user to navigate in a saturated and often uninteresting and non-comprehensive environment. In the medical-clinical context, a lot of information on the web is often incomplete, inaccurate or completely wrong due to an incorrect sharing and a lack of control of the sources. In this context, recommendation systems become essential to filter truthful information and to target users with respect to their needs. The status of recent covid-19 pandemic highlighted the necessity of having health reliable sources. Health recommender systems support user in medical environment to find right information. In this contribution, we report about a project of health recommender system aiming to: (i) aggregate similar users and (ii) guarantee the truthful and quality of the extracted information through a check of the sources and a validation by the medical scientific community. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGBioinformatics Record Association for Computing Machinery

Social health recommender system

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 Copyright is held by the owner/author(s)
ISSN
2331-9291
eISSN
2159-1210
DOI
10.1145/3411750.3411752
Publisher site
See Article on Publisher Site

Abstract

The amount of information and services available on the web represents an important opportunity for people to enrich and share knowledges. The easy access and the continuous growth of data in the web are responsible of an overload of information that leads the user to navigate in a saturated and often uninteresting and non-comprehensive environment. In the medical-clinical context, a lot of information on the web is often incomplete, inaccurate or completely wrong due to an incorrect sharing and a lack of control of the sources. In this context, recommendation systems become essential to filter truthful information and to target users with respect to their needs. The status of recent covid-19 pandemic highlighted the necessity of having health reliable sources. Health recommender systems support user in medical environment to find right information. In this contribution, we report about a project of health recommender system aiming to: (i) aggregate similar users and (ii) guarantee the truthful and quality of the extracted information through a check of the sources and a validation by the medical scientific community.

Journal

ACM SIGBioinformatics Record – Association for Computing Machinery

Published: Jul 22, 2020

Keywords: health

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