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A Dynamics Recommendation System Based on Log Mining.

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Bibliographic Details
Title: A Dynamics Recommendation System Based on Log Mining.
Authors: Gao, Wen, Wang, Shi, Liu, Bin
Source: International Journal of Foundations of Computer Science. Aug2002, Vol. 13 Issue 4, p521. 10p. 5 Diagrams, 3 Charts.
Subjects: Website authoring programs, Web development, Web portals
Abstract: This paper presents a new real-time, dynamic web page recommendation system based on web-log mining. The visit sequences of previous visitors are used to train a classifier for web page recommendation. The recommendation engine identifies a current active user, and submits its visit sequence as an input to the classifier. The output of the recommendation engine is a set of recommended web pages, whose links are attached to bottom of the requested page. Our experiments show that the proposed approach is effective: the predictive accuracy is quite high (over 90%), and the time for the recommendation is quite small. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Foundations of Computer Science is the property of World Scientific Publishing Company 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 Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 7344418
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: A Dynamics Recommendation System Based on Log Mining.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Gao%2C+Wen%22">Gao, Wen</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Shi%22">Wang, Shi</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Bin%22">Liu, Bin</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Foundations+of+Computer+Science%22">International Journal of Foundations of Computer Science</searchLink>. Aug2002, Vol. 13 Issue 4, p521. 10p. 5 Diagrams, 3 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Website+authoring+programs%22">Website authoring programs</searchLink><br /><searchLink fieldCode="DE" term="%22Web+development%22">Web development</searchLink><br /><searchLink fieldCode="DE" term="%22Web+portals%22">Web portals</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper presents a new real-time, dynamic web page recommendation system based on web-log mining. The visit sequences of previous visitors are used to train a classifier for web page recommendation. The recommendation engine identifies a current active user, and submits its visit sequence as an input to the classifier. The output of the recommendation engine is a set of recommended web pages, whose links are attached to bottom of the requested page. Our experiments show that the proposed approach is effective: the predictive accuracy is quite high (over 90%), and the time for the recommendation is quite small. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Foundations of Computer Science is the property of World Scientific Publishing Company 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=7344418
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        Value: 10.1142/S0129054102001254
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      – Code: eng
        Text: English
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        Type: general
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      – SubjectFull: Web portals
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      – TitleFull: A Dynamics Recommendation System Based on Log Mining.
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            NameFull: Gao, Wen
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            NameFull: Wang, Shi
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              M: 08
              Text: Aug2002
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              Y: 2002
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