View in EDS PDF Full Text

Adaptive delivery of E-commerce web sites.

Saved in:
Bibliographic Details
Title: Adaptive delivery of E-commerce web sites.
Authors: Gupta, Ashish, Mathur, Ajay
Source: Intelligent Data Analysis. 2002, Vol. 6 Issue 5, p469. 12p.
Subjects: Electronic commerce, Websites
Abstract: Most e-commerce web sites are very large, often confusing and overwhelm the visitor with a huge amount of information. People cannot easily find what they are looking for. Moreover, the web site is presented in the same format to every visitor, irrespective of his needs. This paper proposes a model to solve the above problems. Our model divides the visitors into groups. Then it arranges web pages in the decreasing order of preference for each group and applies path prediction to find sink pages for that group. The results of these algorithms are displayed in a separate frame without modifying the site. Secondly, our paper addresses the need to guide visitors during the configuration of a product. We propose to apply data mining on the quotes for every group and find associations between different components of the product. We can then display these results as suggestions while the prospective buyer is configuring the product. These suggestions will dynamically change as each selection is made. We have discussed the data mining algorithms applicable to our model. [ABSTRACT FROM AUTHOR]
Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 8769735
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Adaptive delivery of E-commerce web sites.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gupta%2C+Ashish%22">Gupta, Ashish</searchLink><br /><searchLink fieldCode="AR" term="%22Mathur%2C+Ajay%22">Mathur, Ajay</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Intelligent+Data+Analysis%22">Intelligent Data Analysis</searchLink>. 2002, Vol. 6 Issue 5, p469. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Electronic+commerce%22">Electronic commerce</searchLink><br /><searchLink fieldCode="DE" term="%22Websites%22">Websites</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Most e-commerce web sites are very large, often confusing and overwhelm the visitor with a huge amount of information. People cannot easily find what they are looking for. Moreover, the web site is presented in the same format to every visitor, irrespective of his needs. This paper proposes a model to solve the above problems. Our model divides the visitors into groups. Then it arranges web pages in the decreasing order of preference for each group and applies path prediction to find sink pages for that group. The results of these algorithms are displayed in a separate frame without modifying the site. Secondly, our paper addresses the need to guide visitors during the configuration of a product. We propose to apply data mining on the quotes for every group and find associations between different components of the product. We can then display these results as suggestions while the prospective buyer is configuring the product. These suggestions will dynamically change as each selection is made. We have discussed the data mining algorithms applicable to our model. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=8769735
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3233/IDA-2002-6506
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 469
    Subjects:
      – SubjectFull: Electronic commerce
        Type: general
      – SubjectFull: Websites
        Type: general
    Titles:
      – TitleFull: Adaptive delivery of E-commerce web sites.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Gupta, Ashish
      – PersonEntity:
          Name:
            NameFull: Mathur, Ajay
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: 2002
              Type: published
              Y: 2002
          Identifiers:
            – Type: issn-print
              Value: 1088467X
          Numbering:
            – Type: volume
              Value: 6
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Intelligent Data Analysis
              Type: main
ResultId 1