• Login
    View Item 
    •   Home
    • Colleges, Departments, and Organizations
    • Digital Library of Information Science & Technology (DLIST)
    • DLIST
    • View Item
    •   Home
    • Colleges, Departments, and Organizations
    • Digital Library of Information Science & Technology (DLIST)
    • DLIST
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    A Graph Model for E-Commerce Recommender Systems

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    huang2.pdf
    Size:
    448.4Kb
    Format:
    PDF
    Download
    Author
    Huang, Zan
    Chung, Wingyan
    Chen, Hsinchun
    Issue Date
    2004
    Submitted date
    2004-08-20
    Keywords
    Data Mining
    Information Extraction
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial Intelligence lab
    AI lab
    Information retrieval
    E-commerce
    
    Metadata
    Show full item record
    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
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105683
    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
    Collections
    DLIST

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.