Persistent Link:
http://hdl.handle.net/10150/105683
Title:
A Graph Model for E-Commerce Recommender Systems
Author:
Huang, Zan; Chung, Wingyan; Chen, Hsinchun
Citation:
A Graph Model for E-Commerce Recommender Systems 2004, 55(3):259-274 Journal of the American Society for Information Science & Technology
Publisher:
Wiley Periodicals, Inc
Journal:
Journal of the American Society for Information Science & Technology
Issue Date:
2004
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105683
Submitted date:
2004-08-20
Abstract:
Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customersâ preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Data Mining; Information Extraction
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Information retrieval; E-commerce

Full metadata record

DC FieldValue Language
dc.contributor.authorHuang, Zanen_US
dc.contributor.authorChung, Wingyanen_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-08-20T00:00:01Z-
dc.date.available2010-06-18T23:31:49Z-
dc.date.issued2004en_US
dc.date.submitted2004-08-20en_US
dc.identifier.citationA Graph Model for E-Commerce Recommender Systems 2004, 55(3):259-274 Journal of the American Society for Information Science & Technologyen_US
dc.identifier.urihttp://hdl.handle.net/10150/105683-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractInformation overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customersâ preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectData Miningen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInformation retrievalen_US
dc.subject.otherE-commerceen_US
dc.titleA Graph Model for E-Commerce Recommender Systemsen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Science & Technologyen_US
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