Hierarchy Visualization for Group Recommender Systems

Wang, W; Zhang, G; Lu, J

Hierarchy Visualization for Group Recommender Systems

Wang, W

Zhang, G

Lu, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49 (6), pp. 1152 - 1163
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Wang, W https://orcid.org/0000-0001-8727-6090	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.available	2020-05-25T19:07:27Z
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49 (6), pp. 1152 - 1163	en_US
dc.identifier.issn	2168-2216	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124881
dc.description.abstract	© 2013 IEEE. Most recommender systems (RSs), especially group RSs, focus on methods and accuracy but lack explanations, hence users find them difficult to trust. We present a hierarchy visualization method for group recommender (HVGR) systems to provide visual presentation and intuitive explanation. We first use a hierarchy graph to organize all the entities using nodes (e.g., neighbor nodes and recommendation nodes) and illustrate the overall recommender process using edges. Second, a pie chart is attached to every entity node in which each slice represents a single member, which makes it easy to track the influence of each member on a specific entity. HVGR can be extended to adapt different pseudouser modeling methods by resizing group member nodes and pseudouser nodes. It can also be easily extended to individual RSs through the use of a single member group. An implementation has been developed and feasibility is tested using a real data set.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150101645
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems	en_US
dc.relation.isbasedon	10.1109/TSMC.2017.2699690	en_US
dc.title	Hierarchy Visualization for Group Recommender Systems	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	49	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

© 2013 IEEE. Most recommender systems (RSs), especially group RSs, focus on methods and accuracy but lack explanations, hence users find them difficult to trust. We present a hierarchy visualization method for group recommender (HVGR) systems to provide visual presentation and intuitive explanation. We first use a hierarchy graph to organize all the entities using nodes (e.g., neighbor nodes and recommendation nodes) and illustrate the overall recommender process using edges. Second, a pie chart is attached to every entity node in which each slice represents a single member, which makes it easy to track the influence of each member on a specific entity. HVGR can be extended to adapt different pseudouser modeling methods by resizing group member nodes and pseudouser nodes. It can also be easily extended to individual RSs through the use of a single member group. An implementation has been developed and feasibility is tested using a real data set.

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http://hdl.handle.net/10453/124881