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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 /
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 basi...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2006.
|
| Edición: | 1st ed. 2006. |
| Colección: | Lecture Notes in Artificial Intelligence,
3932 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 024 | 7 | |a 10.1007/11899402 |2 doi | |
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| 245 | 1 | 0 | |a Advances in Web Mining and Web Usage Analysis |h [electronic resource] : |b 6th International Workshop on Knowledge Discovery on the Web, WEBKDD 2004, Seattle, WA, USA, August 22-25, 2004, Revised Selected Papers / |c edited by Bamshad Mobasher, Olfa Nasraoui, Bing Liu, Brij Masand. |
| 250 | |a 1st ed. 2006. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2006. | |
| 300 | |a X, 189 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 3932 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Information storage and retrieval systems. | |
| 650 | 0 | |a Application software. | |
| 650 | 0 | |a Computers and civilization. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 650 | 2 | 4 | |a Database Management. |
| 650 | 2 | 4 | |a Information Storage and Retrieval. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a Computers and Society. |
| 700 | 1 | |a Mobasher, Bamshad. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Nasraoui, Olfa. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Liu, Bing. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Masand, Brij. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783540831792 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540471271 |
| 830 | 0 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 3932 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/11899402 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||