Improving dental care recommendation systems using trust and social networks

Pradhan, S; Gay, V; Nepal, S

Improving dental care recommendation systems using trust and social networks

Pradhan, S Gay, V

Nepal, S

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Publication Type:: Conference Proceeding
Citation:: 2014 IEEE International Conference on Communications, ICC 2014, 2014, pp. 4264 - 4269
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Pradhan, S	en_US
dc.contributor.author	Gay, V https://orcid.org/0000-0002-4301-1995	en_US
dc.contributor.author	Nepal, S	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	2014 IEEE International Conference on Communications, ICC 2014, 2014, pp. 4264 - 4269	en_US
dc.identifier.isbn	9781479920037	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33000
dc.description.abstract	The growing popularity of Health Social Networking sites has a tremendous impact on people's health related experiences. However, without any quality filtering, there could be a detrimental effect on the users' health. Trust-based techniques have been identified as effective methods to filter the information for recommendation systems. This research focuses on dental care related social networks and recommendation systems. Trust is critical when choosing a dental care provider due to the invasive nature of the treatment. Surprisingly, current dental care recommendation systems do not use trust-based techniques, and most of them are simple reviews and ratings sites. This research aims at improving dental care recommendation systems by proposing a new framework, taking trust into account. It derives trust from both users' social networks and from existing crowdsourced information on dental care. Such a framework could be used for other healthcare recommendation systems where trust is of major importance. © 2014 IEEE.	en_US
dc.relation.ispartof	2014 IEEE International Conference on Communications, ICC 2014	en_US
dc.relation.isbasedon	10.1109/ICC.2014.6883990	en_US
dc.title	Improving dental care recommendation systems using trust and social networks	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	IEEE
dc.location.activity	Sydney
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

The growing popularity of Health Social Networking sites has a tremendous impact on people's health related experiences. However, without any quality filtering, there could be a detrimental effect on the users' health. Trust-based techniques have been identified as effective methods to filter the information for recommendation systems. This research focuses on dental care related social networks and recommendation systems. Trust is critical when choosing a dental care provider due to the invasive nature of the treatment. Surprisingly, current dental care recommendation systems do not use trust-based techniques, and most of them are simple reviews and ratings sites. This research aims at improving dental care recommendation systems by proposing a new framework, taking trust into account. It derives trust from both users' social networks and from existing crowdsourced information on dental care. Such a framework could be used for other healthcare recommendation systems where trust is of major importance. © 2014 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/33000