Automatic personalization based on Web usage mining

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Mobasher, Bamshad, Cooley, Robert, Srivastava, Jaideep·2000·Communications of the ACM

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The article focuses on cognitive modeling for games and animation    The article deals with the issue of automatic personalization based on web usage mining. Most personalization systems for web fall into three major categories, manual decision rule systems, collaborative filtering systems, and content based filtering agents. The new generation of web personalization tools is attempting to incorporate techniques for pattern discovery from web usage data. Principal elements of web personalization of category include the modeling of web objects and subjects, their categorization, and determination of set of actions to be recommended. The discovery of patterns from usage data is primary in this, however, is not sufficient for performing personalization tasks. A variety of clustering techniques must be used in this analysis. The task of recommendation engine is to compute a recommendation set for the current user session, based on the user profile. It is here that web usage mining holds principal magnitude. This model of web personalization has applications even outside of electronic-commerce. INSETS: Data Preparation for Web Usage Mining; Experiments with the WebPersonalize System; Mining Association Rules for Personalization.

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Authors: Mobasher, B., Cooley, R., & Srivastava, J.
Venue: Communications of the ACM
Publisher: Association for Computing Machinery (ACM)
Published: 2000-08
Volume: 43
Issue: 8
Pages: 142-151
ISSN: 0001-0782 (ISSN-L)
1557-7317
DOI: 10.1145/345124.345169
OpenAlex: W2080950999
Cited by: 1,223
References: 0
FWCI: 127.64
Type: journal article
Language: en
Open access: bronze

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Automatic personalization based on Web usage mining

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