Advances in web mining and web usage analysis : 6th International Workshop on Knowledge Discovery on the Web, WebKDD 2004, Seattle, WA, USA, August 22-25, 2004 : revised selected papers / Bamshad Mobasher [and others].

By: WebKDD 2004 (2004 : Seattle, Wash.)
Contributor(s): Mobasher, Bamshad
Material type: TextTextSeries: SerienbezeichnungLNCS sublibrary: ; Lecture notes in computer science: 3932.; Lecture notes in computer science: ; Hot topics (Berlin, Germany): Publisher: Berlin ; New York : Springer, ©2006Description: 1 online resource (x, 187 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783540471288; 3540471286; 3540471278; 9783540471271Other title: WebKDD 2004 | 6th International Workshop on Knowledge Discovery on the Web | Sixth International Workshop on Knowledge Discovery on the Web | International Workshop on Knowledge Discovery on the WebSubject(s): Web usage mining -- Congresses | Internet users -- Congresses | Internet users | Web usage mining | Informatique | Internet users | Web usage mining | World wide web | Datamining | Kunstmatige intelligentieGenre/Form: Electronic books. | Conference papers and proceedings. Additional physical formats: Print version:: Advances in web mining and web usage analysis.DDC classification: 006.3 LOC classification: ZA4235 | .W43 2004ebOther classification: 54.84 | TP18-532 | SS 4800 | ST 205 | DAT 616f Online resources: Click here to access online
Contents:
Web Usage Analysis and User Modeling -- Mining Temporally Changing Web Usage Graphs -- Improving the Web Usage Analysis Process: A UML Model of the ETL Process -- Web Personalization and Recommender Systems -- Mission-Based Navigational Behaviour Modeling for Web Recommender Systems -- Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors -- Collaborative Quality Filtering: Establishing Consensus or Recovering Ground Truth? -- Search Personalization -- Spying Out Accurate User Preferences for Search Engine Adaptation -- Using Hyperlink Features to Personalize Web Search -- Semantic Web Mining -- Discovering Links Between Lexical and Surface Features in Questions and Answers -- Integrating Web Conceptual Modeling and Web Usage Mining -- Boosting for Text Classification with Semantic Features -- Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts.
Summary: TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage, and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.
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Includes bibliographical references and index.

Print version record.

Web Usage Analysis and User Modeling -- Mining Temporally Changing Web Usage Graphs -- Improving the Web Usage Analysis Process: A UML Model of the ETL Process -- Web Personalization and Recommender Systems -- Mission-Based Navigational Behaviour Modeling for Web Recommender Systems -- Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors -- Collaborative Quality Filtering: Establishing Consensus or Recovering Ground Truth? -- Search Personalization -- Spying Out Accurate User Preferences for Search Engine Adaptation -- Using Hyperlink Features to Personalize Web Search -- Semantic Web Mining -- Discovering Links Between Lexical and Surface Features in Questions and Answers -- Integrating Web Conceptual Modeling and Web Usage Mining -- Boosting for Text Classification with Semantic Features -- Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts.

TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage, and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.

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