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Text learning for user profiling in e-commerce.

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Title: Text learning for user profiling in e-commerce.
Authors: Degemmis, M.1 degemmis@di.uniba.it, Lops, P.1, Ferilli, S.1, Di Mauro, N.1, Basile, T. M. A.1, Semeraro, G.1
Source: International Journal of Systems Science. 10/20/2006, Vol. 37 Issue 13, p905-918. 14p. 10 Charts, 3 Graphs.
Subjects: Electronic commerce, Machine learning, Artificial intelligence, Consumer profiling, Logic programming, Algorithms, Websites, Online library catalogs
Abstract: Exploring digital collections to find information relevant to a user's interests is a challenging task. Algorithms designed to solve this relevant information problem base their relevance computations on user profiles in which representations of the users' interests are maintained. This article presents a new method, based on the classic Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalog of e-commerce Web sites. Experiments have been carried out on several data sets, and results have been compared with those obtained using an inductive logic programming (ILP) approach and a probabilistic one. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd 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:
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DbLabel: Engineering Source
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AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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IllustrationInfo
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  Data: Text learning for user profiling in e-commerce.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Systems+Science%22">International Journal of Systems Science</searchLink>. 10/20/2006, Vol. 37 Issue 13, p905-918. 14p. 10 Charts, 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Electronic+commerce%22">Electronic commerce</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+profiling%22">Consumer profiling</searchLink><br /><searchLink fieldCode="DE" term="%22Logic+programming%22">Logic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Websites%22">Websites</searchLink><br /><searchLink fieldCode="DE" term="%22Online+library+catalogs%22">Online library catalogs</searchLink>
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  Data: Exploring digital collections to find information relevant to a user's interests is a challenging task. Algorithms designed to solve this relevant information problem base their relevance computations on user profiles in which representations of the users' interests are maintained. This article presents a new method, based on the classic Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalog of e-commerce Web sites. Experiments have been carried out on several data sets, and results have been compared with those obtained using an inductive logic programming (ILP) approach and a probabilistic one. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd 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.)
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        Value: 10.1080/00207720600891794
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        Text: English
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      – SubjectFull: Electronic commerce
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Consumer profiling
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              Text: 10/20/2006
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