LEADER 06100nam 22007935 450 001 996465596503316 005 20200630092928.0 010 $a3-540-47128-6 024 7 $a10.1007/11899402 035 $a(CKB)1000000000284056 035 $a(SSID)ssj0000321041 035 $a(PQKBManifestationID)11231147 035 $a(PQKBTitleCode)TC0000321041 035 $a(PQKBWorkID)10276814 035 $a(PQKB)11737929 035 $a(DE-He213)978-3-540-47128-8 035 $a(MiAaPQ)EBC3068556 035 $a(PPN)123139147 035 $a(EXLCZ)991000000000284056 100 $a20100301d2006 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in Web Mining and Web Usage Analysis$b[electronic resource] $e6th International Workshop on Knowledge Discovery on the Web, WEBKDD 2004, Seattle, WA, USA, August 22-25, 2004, Revised Selected Papers /$fedited by Bamshad Mobasher, Olfa Nasraoui, Bing Liu, Brij Masand 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (X, 189 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3932 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-47127-8 320 $aIncludes bibliographical references and index. 327 $aWeb 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. 330 $aTheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage,and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications. 410 0$aLecture Notes in Artificial Intelligence ;$v3932 606 $aArtificial intelligence 606 $aComputer communication systems 606 $aDatabase management 606 $aInformation storage and retrieval 606 $aApplication software 606 $aComputers and civilization 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aComputers and Society$3https://scigraph.springernature.com/ontologies/product-market-codes/I24040 615 0$aArtificial intelligence. 615 0$aComputer communication systems. 615 0$aDatabase management. 615 0$aInformation storage and retrieval. 615 0$aApplication software. 615 0$aComputers and civilization. 615 14$aArtificial Intelligence. 615 24$aComputer Communication Networks. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aComputers and Society. 676 $a006.3 702 $aMobasher$b Bamshad$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNasraoui$b Olfa$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLiu$b Bing$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMasand$b Brij$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996465596503316 996 $aAdvances in Web Mining and Web Usage Analysis$9772002 997 $aUNISA