Recommeder system based on consumer product reviews

Silvana, A; Zhang, DD; Simoff, SJ; Debenham, JK

Recommeder system based on consumer product reviews

Silvana, A Zhang, DD Simoff, SJ Debenham, JK

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceeding of the 2006 IEEE/WIC/ACM International conference on web intelligence ( WI 2006 Main conference proceedings)(WI'06), 2006, pp. 1 - 5
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Silvana, A	en_US
dc.contributor.author	Zhang, DD	en_US
dc.contributor.author	Simoff, SJ	en_US
dc.contributor.author	Debenham, JK	en_US
dc.contributor.editor	Unknown	en_US
dc.date	2006-12-18	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	Proceeding of the 2006 IEEE/WIC/ACM International conference on web intelligence ( WI 2006 Main conference proceedings)(WI'06), 2006, pp. 1 - 5	en_US
dc.identifier.isbn	0-7695-2750-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2676
dc.description.abstract	Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceeding of the 2006 IEEE/WIC/ACM International conference on web intelligence ( WI 2006 Main conference proceedings)(WI'06)	en_US
dc.relation.ispartof	IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology	en_US
dc.relation.isbasedon	10.1109/WI.2006.144	en_US
dc.rights	© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Recommeder system based on consumer product reviews	en_US
dc.type	Conference Proceeding
utslib.location	HK China	en_US
utslib.location.activity	HK China	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	HK China	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 Software
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
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.place-of-publication	HK China	en_US
pubs.start-date	2006-12-18	en_US

Abstract:

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras.

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http://hdl.handle.net/10453/2676