A graph model for e-commerce recommender systems

Zan Huang, Wingyan Chung, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

151 Scopus citations

Abstract

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customer's 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.

Original languageEnglish (US)
Pages (from-to)259-274
Number of pages16
JournalJournal of the American Society for Information Science and Technology
Volume55
Issue number3
DOIs
StatePublished - Feb 1 2004

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence

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