Serveur d'exploration sur la musique en Sarre

Attention, ce site est en cours de développement !
Attention, site généré par des moyens informatiques à partir de corpus bruts.
Les informations ne sont donc pas validées.

Towards adaptive Web sites: Conceptual framework and case study

Identifieur interne : 000021 ( Istex/Corpus ); précédent : 000020; suivant : 000022

Towards adaptive Web sites: Conceptual framework and case study

Auteurs : Mike Perkowitz ; Oren Etzioni

Source :

RBID : ISTEX:01ECA0C769D7222C51005A35F9E4B6403E3EE5F2

English descriptors

Abstract

Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites.To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.

Url:
DOI: 10.1016/S0004-3702(99)00098-3

Links to Exploration step

ISTEX:01ECA0C769D7222C51005A35F9E4B6403E3EE5F2

Le document en format XML

<record>
<TEI wicri:istexFullTextTei="biblStruct">
<teiHeader>
<fileDesc>
<titleStmt>
<title xml:lang="en">Towards adaptive Web sites: Conceptual framework and case study</title>
<author>
<name sortKey="Perkowitz, Mike" sort="Perkowitz, Mike" uniqKey="Perkowitz M" first="Mike" last="Perkowitz">Mike Perkowitz</name>
<affiliation>
<mods:affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</mods:affiliation>
</affiliation>
<affiliation>
<mods:affiliation>E-mail: map@cs.washington.edu</mods:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Etzioni, Oren" sort="Etzioni, Oren" uniqKey="Etzioni O" first="Oren" last="Etzioni">Oren Etzioni</name>
<affiliation>
<mods:affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</mods:affiliation>
</affiliation>
<affiliation>
<mods:affiliation>E-mail: map@cs.washington.edu</mods:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt>
<idno type="wicri:source">ISTEX</idno>
<idno type="RBID">ISTEX:01ECA0C769D7222C51005A35F9E4B6403E3EE5F2</idno>
<date when="2000" year="2000">2000</date>
<idno type="doi">10.1016/S0004-3702(99)00098-3</idno>
<idno type="url">https://api.istex.fr/document/01ECA0C769D7222C51005A35F9E4B6403E3EE5F2/fulltext/pdf</idno>
<idno type="wicri:Area/Istex/Corpus">000021</idno>
<idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">000021</idno>
</publicationStmt>
<sourceDesc>
<biblStruct>
<analytic>
<title level="a" type="main" xml:lang="en">Towards adaptive Web sites: Conceptual framework and case study</title>
<author>
<name sortKey="Perkowitz, Mike" sort="Perkowitz, Mike" uniqKey="Perkowitz M" first="Mike" last="Perkowitz">Mike Perkowitz</name>
<affiliation>
<mods:affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</mods:affiliation>
</affiliation>
<affiliation>
<mods:affiliation>E-mail: map@cs.washington.edu</mods:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Etzioni, Oren" sort="Etzioni, Oren" uniqKey="Etzioni O" first="Oren" last="Etzioni">Oren Etzioni</name>
<affiliation>
<mods:affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</mods:affiliation>
</affiliation>
<affiliation>
<mods:affiliation>E-mail: map@cs.washington.edu</mods:affiliation>
</affiliation>
</author>
</analytic>
<monogr></monogr>
<series>
<title level="j">Artificial Intelligence</title>
<title level="j" type="abbrev">ARTINT</title>
<idno type="ISSN">0004-3702</idno>
<imprint>
<publisher>ELSEVIER</publisher>
<date type="published" when="2000">2000</date>
<biblScope unit="volume">118</biblScope>
<biblScope unit="issue">1–2</biblScope>
<biblScope unit="page" from="245">245</biblScope>
<biblScope unit="page" to="275">275</biblScope>
</imprint>
<idno type="ISSN">0004-3702</idno>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt>
<idno type="ISSN">0004-3702</idno>
</seriesStmt>
</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="KwdEn" xml:lang="en">
<term>Adaptive Web sites</term>
<term>Conceptual clustering</term>
<term>Data mining</term>
</keywords>
<keywords scheme="Teeft" xml:lang="en">
<term>Access logs</term>
<term>Adaptive</term>
<term>Adaptive sites</term>
<term>Algorithm</term>
<term>Apriori</term>
<term>Association rules</term>
<term>Audio samples</term>
<term>Avanti</term>
<term>Avanti project</term>
<term>Average pairwise</term>
<term>Average pairwise similarity</term>
<term>Candidate clusters</term>
<term>Candidate index pages</term>
<term>Candidate link sets</term>
<term>Clique</term>
<term>Cluster</term>
<term>Cluster mining</term>
<term>Cluster mining algorithms</term>
<term>Cluster mining problem</term>
<term>Cobweb</term>
<term>Cohesive clusters</term>
<term>Collaborative</term>
<term>Common topic</term>
<term>Computer science</term>
<term>Computer science department</term>
<term>Conceptual</term>
<term>Conceptual algorithms</term>
<term>Conceptual approach</term>
<term>Conceptual cluster mining</term>
<term>Conceptual cluster mining problem</term>
<term>Conceptual clusters</term>
<term>Conceptual description</term>
<term>Conceptual descriptions</term>
<term>Conceptual hierarchy</term>
<term>Conceptual language</term>
<term>Conceptual structure</term>
<term>Conjunctive rule</term>
<term>Contribution values</term>
<term>Current visit</term>
<term>Customization</term>
<term>Data collection</term>
<term>Data mining</term>
<term>Data mining algorithms</term>
<term>Description length</term>
<term>Different kinds</term>
<term>Different ways</term>
<term>Distinct pages</term>
<term>Distinct visitors</term>
<term>Document retrieval</term>
<term>Elsevier science</term>
<term>Entire space</term>
<term>Etzioni</term>
<term>Etzioni intelligence</term>
<term>Experimental comparison</term>
<term>Experimental method</term>
<term>Fossils cast</term>
<term>Front page</term>
<term>Full access</term>
<term>Future work</term>
<term>Goal state</term>
<term>Graph algorithms</term>
<term>Hierarchical</term>
<term>Hierarchical agglomerative</term>
<term>Hierarchical partition</term>
<term>High quality clusters</term>
<term>Higher impact</term>
<term>Html pages</term>
<term>Human webmaster</term>
<term>Index page</term>
<term>Index page synthesis</term>
<term>Index page synthesis problem</term>
<term>Index pages</term>
<term>Indexfinder</term>
<term>Indexfinder algorithm</term>
<term>Individual visitors</term>
<term>Initial state</term>
<term>Instantiation</term>
<term>Large collections</term>
<term>Large databases</term>
<term>Large number</term>
<term>Links</term>
<term>Links users</term>
<term>Ltering</term>
<term>Many clusters</term>
<term>Many kinds</term>
<term>Many pages</term>
<term>Many people</term>
<term>Many visitors</term>
<term>Matrix</term>
<term>Maximal cliques</term>
<term>Maximum rule length</term>
<term>Mining</term>
<term>Mining association rules</term>
<term>Music machines</term>
<term>Music machines site</term>
<term>Nding</term>
<term>Negative examples</term>
<term>Nontrivial adaptations</term>
<term>Object space</term>
<term>Original design</term>
<term>Other algorithms</term>
<term>Other hand</term>
<term>Other members</term>
<term>Other users</term>
<term>Overlap elimination</term>
<term>Overlap measure</term>
<term>Page frequencies</term>
<term>Page views</term>
<term>Pagegather</term>
<term>Pagegather algorithm</term>
<term>Pairwise</term>
<term>Pairwise similarity measure</term>
<term>Particular topic</term>
<term>Particular type</term>
<term>Perkowitz</term>
<term>Personal agents</term>
<term>Pgcc</term>
<term>Pgcc pgclique</term>
<term>Pgclique</term>
<term>Pgmdl</term>
<term>Pgrip pgpos</term>
<term>Popular links</term>
<term>Positive examples</term>
<term>Presentation agent</term>
<term>Previous section</term>
<term>Previous work</term>
<term>Primary testbed</term>
<term>Problem domain</term>
<term>Problem space</term>
<term>Proc</term>
<term>Quality measure</term>
<term>Quality threshold</term>
<term>Reasonable size</term>
<term>Scml</term>
<term>Scml algorithm schema</term>
<term>Scml instantiations</term>
<term>Scml schema</term>
<term>Search space</term>
<term>Second site</term>
<term>Server</term>
<term>Server logs</term>
<term>Sigmod conference</term>
<term>Similar objects</term>
<term>Similar tastes</term>
<term>Similarity matrix</term>
<term>Similarity measure</term>
<term>Single cluster</term>
<term>Single session</term>
<term>Single visit</term>
<term>Small number</term>
<term>State transitions</term>
<term>Statistical approaches</term>
<term>Statistical cluster</term>
<term>Statistical cluster mining algorithm</term>
<term>Test data</term>
<term>Total number</term>
<term>Tractable algorithm</term>
<term>Training data</term>
<term>Unlinked pages</term>
<term>Unprocessed results</term>
<term>User</term>
<term>User access patterns</term>
<term>User clicks</term>
<term>User model</term>
<term>User visits</term>
<term>Visitor</term>
<term>Visitor access patterns</term>
<term>Vldb conference</term>
<term>Webmaster</term>
<term>Webwatcher</term>
<term>Word vectors</term>
</keywords>
</textClass>
<langUsage>
<language ident="en">en</language>
</langUsage>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites.To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.</div>
</front>
</TEI>
<istex>
<corpusName>elsevier</corpusName>
<keywords>
<teeft>
<json:string>algorithm</json:string>
<json:string>pagegather</json:string>
<json:string>index pages</json:string>
<json:string>etzioni</json:string>
<json:string>perkowitz</json:string>
<json:string>adaptive</json:string>
<json:string>etzioni intelligence</json:string>
<json:string>webmaster</json:string>
<json:string>music machines</json:string>
<json:string>proc</json:string>
<json:string>apriori</json:string>
<json:string>indexfinder</json:string>
<json:string>cobweb</json:string>
<json:string>pgclique</json:string>
<json:string>scml</json:string>
<json:string>nding</json:string>
<json:string>pgcc</json:string>
<json:string>customization</json:string>
<json:string>user</json:string>
<json:string>collaborative</json:string>
<json:string>ltering</json:string>
<json:string>index page</json:string>
<json:string>data collection</json:string>
<json:string>instantiation</json:string>
<json:string>access logs</json:string>
<json:string>previous work</json:string>
<json:string>server</json:string>
<json:string>pagegather algorithm</json:string>
<json:string>pgmdl</json:string>
<json:string>cluster mining</json:string>
<json:string>index page synthesis</json:string>
<json:string>pairwise</json:string>
<json:string>similarity matrix</json:string>
<json:string>hierarchical</json:string>
<json:string>webwatcher</json:string>
<json:string>avanti</json:string>
<json:string>matrix</json:string>
<json:string>cluster</json:string>
<json:string>conceptual</json:string>
<json:string>page views</json:string>
<json:string>scml algorithm schema</json:string>
<json:string>training data</json:string>
<json:string>conceptual descriptions</json:string>
<json:string>conceptual cluster mining problem</json:string>
<json:string>data mining</json:string>
<json:string>positive examples</json:string>
<json:string>conceptual description</json:string>
<json:string>human webmaster</json:string>
<json:string>music machines site</json:string>
<json:string>test data</json:string>
<json:string>clique</json:string>
<json:string>front page</json:string>
<json:string>overlap elimination</json:string>
<json:string>index page synthesis problem</json:string>
<json:string>user visits</json:string>
<json:string>other users</json:string>
<json:string>statistical approaches</json:string>
<json:string>conceptual cluster mining</json:string>
<json:string>conceptual language</json:string>
<json:string>user model</json:string>
<json:string>other hand</json:string>
<json:string>cluster mining problem</json:string>
<json:string>user access patterns</json:string>
<json:string>links</json:string>
<json:string>state transitions</json:string>
<json:string>distinct visitors</json:string>
<json:string>pairwise similarity measure</json:string>
<json:string>small number</json:string>
<json:string>unlinked pages</json:string>
<json:string>graph algorithms</json:string>
<json:string>vldb conference</json:string>
<json:string>similar tastes</json:string>
<json:string>visitor access patterns</json:string>
<json:string>many clusters</json:string>
<json:string>large number</json:string>
<json:string>distinct pages</json:string>
<json:string>initial state</json:string>
<json:string>quality measure</json:string>
<json:string>indexfinder algorithm</json:string>
<json:string>object space</json:string>
<json:string>future work</json:string>
<json:string>cluster mining algorithms</json:string>
<json:string>conceptual clusters</json:string>
<json:string>negative examples</json:string>
<json:string>candidate index pages</json:string>
<json:string>similarity measure</json:string>
<json:string>mining association rules</json:string>
<json:string>many people</json:string>
<json:string>search space</json:string>
<json:string>reasonable size</json:string>
<json:string>word vectors</json:string>
<json:string>hierarchical agglomerative</json:string>
<json:string>computer science</json:string>
<json:string>goal state</json:string>
<json:string>large collections</json:string>
<json:string>elsevier science</json:string>
<json:string>particular topic</json:string>
<json:string>entire space</json:string>
<json:string>current visit</json:string>
<json:string>high quality clusters</json:string>
<json:string>adaptive sites</json:string>
<json:string>association rules</json:string>
<json:string>page frequencies</json:string>
<json:string>average pairwise similarity</json:string>
<json:string>links users</json:string>
<json:string>experimental method</json:string>
<json:string>second site</json:string>
<json:string>html pages</json:string>
<json:string>audio samples</json:string>
<json:string>fossils cast</json:string>
<json:string>many kinds</json:string>
<json:string>individual visitors</json:string>
<json:string>computer science department</json:string>
<json:string>user clicks</json:string>
<json:string>total number</json:string>
<json:string>many pages</json:string>
<json:string>single session</json:string>
<json:string>different kinds</json:string>
<json:string>problem domain</json:string>
<json:string>common topic</json:string>
<json:string>many visitors</json:string>
<json:string>avanti project</json:string>
<json:string>maximal cliques</json:string>
<json:string>other members</json:string>
<json:string>quality threshold</json:string>
<json:string>single cluster</json:string>
<json:string>original design</json:string>
<json:string>overlap measure</json:string>
<json:string>pgcc pgclique</json:string>
<json:string>candidate clusters</json:string>
<json:string>previous section</json:string>
<json:string>average pairwise</json:string>
<json:string>unprocessed results</json:string>
<json:string>higher impact</json:string>
<json:string>particular type</json:string>
<json:string>candidate link sets</json:string>
<json:string>problem space</json:string>
<json:string>similar objects</json:string>
<json:string>server logs</json:string>
<json:string>statistical cluster mining algorithm</json:string>
<json:string>cohesive clusters</json:string>
<json:string>hierarchical partition</json:string>
<json:string>personal agents</json:string>
<json:string>conceptual structure</json:string>
<json:string>conceptual hierarchy</json:string>
<json:string>contribution values</json:string>
<json:string>presentation agent</json:string>
<json:string>single visit</json:string>
<json:string>tractable algorithm</json:string>
<json:string>statistical cluster</json:string>
<json:string>popular links</json:string>
<json:string>data mining algorithms</json:string>
<json:string>scml schema</json:string>
<json:string>conceptual approach</json:string>
<json:string>conjunctive rule</json:string>
<json:string>maximum rule length</json:string>
<json:string>document retrieval</json:string>
<json:string>description length</json:string>
<json:string>experimental comparison</json:string>
<json:string>scml instantiations</json:string>
<json:string>conceptual algorithms</json:string>
<json:string>nontrivial adaptations</json:string>
<json:string>pgrip pgpos</json:string>
<json:string>other algorithms</json:string>
<json:string>full access</json:string>
<json:string>large databases</json:string>
<json:string>different ways</json:string>
<json:string>sigmod conference</json:string>
<json:string>primary testbed</json:string>
<json:string>mining</json:string>
<json:string>visitor</json:string>
</teeft>
</keywords>
<author>
<json:item>
<name>Mike Perkowitz</name>
<affiliations>
<json:string>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</json:string>
<json:string>E-mail: map@cs.washington.edu</json:string>
</affiliations>
</json:item>
<json:item>
<name>Oren Etzioni</name>
<affiliations>
<json:string>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</json:string>
<json:string>E-mail: map@cs.washington.edu</json:string>
</affiliations>
</json:item>
</author>
<subject>
<json:item>
<lang>
<json:string>eng</json:string>
</lang>
<value>Adaptive Web sites</value>
</json:item>
<json:item>
<lang>
<json:string>eng</json:string>
</lang>
<value>Conceptual clustering</value>
</json:item>
<json:item>
<lang>
<json:string>eng</json:string>
</lang>
<value>Data mining</value>
</json:item>
</subject>
<articleId>
<json:string>1698</json:string>
</articleId>
<language>
<json:string>eng</json:string>
</language>
<originalGenre>
<json:string>Full-length article</json:string>
</originalGenre>
<abstract>Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites.To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.</abstract>
<qualityIndicators>
<score>6.536</score>
<pdfVersion>1.2</pdfVersion>
<pdfPageSize>543 x 744 pts</pdfPageSize>
<refBibsNative>true</refBibsNative>
<keywordCount>3</keywordCount>
<abstractCharCount>905</abstractCharCount>
<pdfWordCount>12766</pdfWordCount>
<pdfCharCount>73716</pdfCharCount>
<pdfPageCount>31</pdfPageCount>
<abstractWordCount>128</abstractWordCount>
</qualityIndicators>
<title>Towards adaptive Web sites: Conceptual framework and case study</title>
<pii>
<json:string>S0004-3702(99)00098-3</json:string>
</pii>
<genre>
<json:string>research-article</json:string>
</genre>
<host>
<title>Artificial Intelligence</title>
<language>
<json:string>unknown</json:string>
</language>
<publicationDate>2000</publicationDate>
<issn>
<json:string>0004-3702</json:string>
</issn>
<pii>
<json:string>S0004-3702(00)X0062-8</json:string>
</pii>
<volume>118</volume>
<issue>1–2</issue>
<pages>
<first>245</first>
<last>275</last>
</pages>
<genre>
<json:string>journal</json:string>
</genre>
</host>
<categories>
<wos>
<json:string>science</json:string>
<json:string>computer science, artificial intelligence</json:string>
</wos>
<scienceMetrix>
<json:string>applied sciences</json:string>
<json:string>information & communication technologies</json:string>
<json:string>artificial intelligence & image processing</json:string>
</scienceMetrix>
<inist>
<json:string>sciences appliquees, technologies et medecines</json:string>
<json:string>sciences exactes et technologie</json:string>
<json:string>sciences et techniques communes</json:string>
<json:string>sciences de l'information. documentation</json:string>
</inist>
</categories>
<publicationDate>2000</publicationDate>
<copyrightDate>2000</copyrightDate>
<doi>
<json:string>10.1016/S0004-3702(99)00098-3</json:string>
</doi>
<id>01ECA0C769D7222C51005A35F9E4B6403E3EE5F2</id>
<score>1</score>
<fulltext>
<json:item>
<extension>pdf</extension>
<original>true</original>
<mimetype>application/pdf</mimetype>
<uri>https://api.istex.fr/document/01ECA0C769D7222C51005A35F9E4B6403E3EE5F2/fulltext/pdf</uri>
</json:item>
<json:item>
<extension>zip</extension>
<original>false</original>
<mimetype>application/zip</mimetype>
<uri>https://api.istex.fr/document/01ECA0C769D7222C51005A35F9E4B6403E3EE5F2/fulltext/zip</uri>
</json:item>
<istex:fulltextTEI uri="https://api.istex.fr/document/01ECA0C769D7222C51005A35F9E4B6403E3EE5F2/fulltext/tei">
<teiHeader>
<fileDesc>
<titleStmt>
<title level="a" type="main" xml:lang="en">Towards adaptive Web sites: Conceptual framework and case study</title>
</titleStmt>
<publicationStmt>
<authority>ISTEX</authority>
<publisher>ELSEVIER</publisher>
<availability>
<p>ELSEVIER</p>
</availability>
<date>2000</date>
</publicationStmt>
<sourceDesc>
<biblStruct type="inbook">
<analytic>
<title level="a" type="main" xml:lang="en">Towards adaptive Web sites: Conceptual framework and case study</title>
<author xml:id="author-0000">
<persName>
<forename type="first">Mike</forename>
<surname>Perkowitz</surname>
</persName>
<email>map@cs.washington.edu</email>
<note type="correspondence">
<p>Corresponding author.</p>
</note>
<affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</affiliation>
</author>
<author xml:id="author-0001">
<persName>
<forename type="first">Oren</forename>
<surname>Etzioni</surname>
</persName>
<email>map@cs.washington.edu</email>
<affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</affiliation>
</author>
<idno type="istex">01ECA0C769D7222C51005A35F9E4B6403E3EE5F2</idno>
<idno type="DOI">10.1016/S0004-3702(99)00098-3</idno>
<idno type="PII">S0004-3702(99)00098-3</idno>
<idno type="ArticleID">1698</idno>
</analytic>
<monogr>
<title level="j">Artificial Intelligence</title>
<title level="j" type="abbrev">ARTINT</title>
<idno type="pISSN">0004-3702</idno>
<idno type="PII">S0004-3702(00)X0062-8</idno>
<imprint>
<publisher>ELSEVIER</publisher>
<date type="published" when="2000"></date>
<biblScope unit="volume">118</biblScope>
<biblScope unit="issue">1–2</biblScope>
<biblScope unit="page" from="245">245</biblScope>
<biblScope unit="page" to="275">275</biblScope>
</imprint>
</monogr>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<creation>
<date>2000</date>
</creation>
<langUsage>
<language ident="en">en</language>
</langUsage>
<abstract xml:lang="en">
<p>Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites.To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.</p>
</abstract>
<textClass xml:lang="en">
<keywords scheme="keyword">
<list>
<head>Keywords</head>
<item>
<term>Adaptive Web sites</term>
</item>
<item>
<term>Conceptual clustering</term>
</item>
<item>
<term>Data mining</term>
</item>
</list>
</keywords>
</textClass>
</profileDesc>
<revisionDesc>
<change when="1999-07-28">Modified</change>
<change when="2000">Published</change>
</revisionDesc>
</teiHeader>
</istex:fulltextTEI>
<json:item>
<extension>txt</extension>
<original>false</original>
<mimetype>text/plain</mimetype>
<uri>https://api.istex.fr/document/01ECA0C769D7222C51005A35F9E4B6403E3EE5F2/fulltext/txt</uri>
</json:item>
</fulltext>
<metadata>
<istex:metadataXml wicri:clean="Elsevier, elements deleted: tail">
<istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration>
<istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"></istex:docType>
<istex:document>
<converted-article version="4.5.2" docsubtype="fla" xml:lang="en">
<item-info>
<jid>ARTINT</jid>
<aid>1698</aid>
<ce:pii>S0004-3702(99)00098-3</ce:pii>
<ce:doi>10.1016/S0004-3702(99)00098-3</ce:doi>
<ce:copyright type="unknown" year="2000"></ce:copyright>
</item-info>
<head>
<ce:title>Towards adaptive Web sites: Conceptual framework and case study</ce:title>
<ce:author-group>
<ce:author>
<ce:given-name>Mike</ce:given-name>
<ce:surname>Perkowitz</ce:surname>
<ce:cross-ref refid="COR1">*</ce:cross-ref>
<ce:e-address type="email">map@cs.washington.edu</ce:e-address>
</ce:author>
<ce:author>
<ce:given-name>Oren</ce:given-name>
<ce:surname>Etzioni</ce:surname>
<ce:e-address type="email">etzioni@cs.washington.edu</ce:e-address>
</ce:author>
<ce:affiliation>
<ce:textfn>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</ce:textfn>
</ce:affiliation>
<ce:correspondence id="COR1">
<ce:label>*</ce:label>
<ce:text>Corresponding author.</ce:text>
</ce:correspondence>
</ce:author-group>
<ce:date-received day="1" month="2" year="1999"></ce:date-received>
<ce:date-revised day="28" month="7" year="1999"></ce:date-revised>
<ce:abstract class="author">
<ce:section-title>Abstract</ce:section-title>
<ce:abstract-sec>
<ce:simple-para view="all" id="simple-para.0010">Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates
<ce:italic>adaptive Web sites</ce:italic>
:
<ce:italic>sites that automatically improve their organization and presentation by learning from visitor access patterns</ce:italic>
. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites.</ce:simple-para>
<ce:simple-para view="all" id="simple-para.0015">To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm,
<math altimg="si1.gif">K</math>
-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.</ce:simple-para>
</ce:abstract-sec>
</ce:abstract>
<ce:keywords class="keyword">
<ce:section-title>Keywords</ce:section-title>
<ce:keyword>
<ce:text>Adaptive Web sites</ce:text>
</ce:keyword>
<ce:keyword>
<ce:text>Conceptual clustering</ce:text>
</ce:keyword>
<ce:keyword>
<ce:text>Data mining</ce:text>
</ce:keyword>
</ce:keywords>
</head>
</converted-article>
</istex:document>
</istex:metadataXml>
<mods version="3.6">
<titleInfo lang="en">
<title>Towards adaptive Web sites: Conceptual framework and case study</title>
</titleInfo>
<titleInfo type="alternative" lang="en" contentType="CDATA">
<title>Towards adaptive Web sites: Conceptual framework and case study</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mike</namePart>
<namePart type="family">Perkowitz</namePart>
<affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</affiliation>
<affiliation>E-mail: map@cs.washington.edu</affiliation>
<description>Corresponding author.</description>
<role>
<roleTerm type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oren</namePart>
<namePart type="family">Etzioni</namePart>
<affiliation>Department of Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA</affiliation>
<affiliation>E-mail: map@cs.washington.edu</affiliation>
<role>
<roleTerm type="text">author</roleTerm>
</role>
</name>
<typeOfResource>text</typeOfResource>
<genre type="research-article" displayLabel="Full-length article"></genre>
<originInfo>
<publisher>ELSEVIER</publisher>
<dateIssued encoding="w3cdtf">2000</dateIssued>
<dateModified encoding="w3cdtf">1999-07-28</dateModified>
<copyrightDate encoding="w3cdtf">2000</copyrightDate>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">eng</languageTerm>
<languageTerm type="code" authority="rfc3066">en</languageTerm>
</language>
<physicalDescription>
<internetMediaType>text/html</internetMediaType>
</physicalDescription>
<abstract lang="en">Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites.To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.</abstract>
<subject lang="en">
<genre>Keywords</genre>
<topic>Adaptive Web sites</topic>
<topic>Conceptual clustering</topic>
<topic>Data mining</topic>
</subject>
<relatedItem type="host">
<titleInfo>
<title>Artificial Intelligence</title>
</titleInfo>
<titleInfo type="abbreviated">
<title>ARTINT</title>
</titleInfo>
<genre type="journal">journal</genre>
<originInfo>
<dateIssued encoding="w3cdtf">200004</dateIssued>
</originInfo>
<identifier type="ISSN">0004-3702</identifier>
<identifier type="PII">S0004-3702(00)X0062-8</identifier>
<part>
<date>200004</date>
<detail type="volume">
<number>118</number>
<caption>vol.</caption>
</detail>
<detail type="issue">
<number>1–2</number>
<caption>no.</caption>
</detail>
<extent unit="issue pages">
<start>1</start>
<end>300</end>
</extent>
<extent unit="pages">
<start>245</start>
<end>275</end>
</extent>
</part>
</relatedItem>
<identifier type="istex">01ECA0C769D7222C51005A35F9E4B6403E3EE5F2</identifier>
<identifier type="DOI">10.1016/S0004-3702(99)00098-3</identifier>
<identifier type="PII">S0004-3702(99)00098-3</identifier>
<identifier type="ArticleID">1698</identifier>
<recordInfo>
<recordContentSource>ELSEVIER</recordContentSource>
</recordInfo>
</mods>
</metadata>
<serie></serie>
</istex>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Sarre/explor/MusicSarreV3/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000021 | SxmlIndent | more

Ou

HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 000021 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Sarre
   |area=    MusicSarreV3
   |flux=    Istex
   |étape=   Corpus
   |type=    RBID
   |clé=     ISTEX:01ECA0C769D7222C51005A35F9E4B6403E3EE5F2
   |texte=   Towards adaptive Web sites: Conceptual framework and case study
}}

Wicri

This area was generated with Dilib version V0.6.33.
Data generation: Sun Jul 15 18:16:09 2018. Site generation: Tue Mar 5 19:21:25 2024