2000•Communications of the ACMOpen access
Automatic personalization based on Web usage mining
Bamshad Mobasher, Robert Cooley, Jaideep Srivastava
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Abstract
The article focuses on cognitive modeling for games and animation <!--[if gte mso 9]> Biblioteca de Ciencias y Tecnología Normal Biblioteca de Ciencias y Tecnología 2 45 2006-05-25T23:16:00Z 2006-05-25T23:16:00Z 1 180 995 UCLA 8 2 1173 11.6568 <![endif]--><!--[if gte mso 9]> Clean Clean 21 false false false MicrosoftInternetExplorer4 <![endif]--><!--[if gte mso 9]> <![endif]--> <!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman";} span.SpellE {mso-style-name:""; mso-spl-e:yes;} @page Section1 {size:595.3pt 841.9pt; margin:70.85pt 3.0cm 70.85pt 3.0cm; mso-header-margin:35.4pt; mso-footer-margin:35.4pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> <!--[if gte mso 10]> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} <![endif]--> 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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