TOAST: A Topic-Oriented Tag-Based Recommender System

Xu, G; Gu, Y; Zhang, Y; Yang, Z; Kitsuregawa, M

TOAST: A Topic-Oriented Tag-Based Recommender System

Xu, G

Gu, Y Zhang, Y Yang, Z Kitsuregawa, M

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Publisher:: Springer Berlin / Heidelberg
Publication Type:: Conference Proceeding
Citation:: Web Information System Engineering WISE 2011 Lecture Notes in Computer Science, 2011, 6997 pp. 158 - 171
Issue Date:: 2011-01

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Field	Value	Language
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Gu, Y	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Yang, Z	en_US
dc.contributor.author	Kitsuregawa, M	en_US
dc.contributor.editor	NA	en_US
dc.date	2011-10-13	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	Web Information System Engineering WISE 2011 Lecture Notes in Computer Science, 2011, 6997 pp. 158 - 171	en_US
dc.identifier.isbn	978-3-642-24433-9	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29146
dc.identifier.uri	http://hdl.handle.net/10453/33788
dc.description.abstract	Social Annotation Systems have emerged as a popular application with the advance of Web 2.0 technologies. Tags generated by users using arbitrary words to express their own opinions and perceptions on various resources provide a new intermediate dimension between users and resources, which deemed to convey the user preference information. Using clustering for topic extraction and incorporating it with the capture of user preference and resource affiliation is becoming an effective practice in tag-based recommender systems. In this paper, we aim to address these challenges via a topic graph approach. We first propose a Topic Oriented Graph (TOG), which models the user preference and resource affiliation on various topics. Based on the graph, we devise a Topic-Oriented Tag-based Recommendation System (TOAST) by using the preference propagation on the graph. We conduct experiments on two real datasets to demonstrate that our approach outperforms other state-of-the-art algorithms.	en_US
dc.publisher	Springer Berlin / Heidelberg	en_US
dc.relation.ispartof	Web Information System Engineering WISE 2011 Lecture Notes in Computer Science	en_US
dc.relation.ispartof	International Conference on Web Information Systems Engineering	en_US
dc.relation.isbasedon	10.1007/978-3-642-24434-6_12	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	TOAST: A Topic-Oriented Tag-Based Recommender System	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6997	en_US
utslib.location	Germany	en_US
utslib.location.activity	Sydney, Australia	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Sydney, Australia	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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2011-10-14	en_US
pubs.place-of-publication	Germany	en_US
pubs.start-date	2011-10-13	en_US
pubs.volume	6997	en_US

Abstract:

Social Annotation Systems have emerged as a popular application with the advance of Web 2.0 technologies. Tags generated by users using arbitrary words to express their own opinions and perceptions on various resources provide a new intermediate dimension between users and resources, which deemed to convey the user preference information. Using clustering for topic extraction and incorporating it with the capture of user preference and resource affiliation is becoming an effective practice in tag-based recommender systems. In this paper, we aim to address these challenges via a topic graph approach. We first propose a Topic Oriented Graph (TOG), which models the user preference and resource affiliation on various topics. Based on the graph, we devise a Topic-Oriented Tag-based Recommendation System (TOAST) by using the preference propagation on the graph. We conduct experiments on two real datasets to demonstrate that our approach outperforms other state-of-the-art algorithms.

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http://hdl.handle.net/10453/29146