Advances in Web Mining and Web Usage Analysis

1. Front Matter

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2. Web Usage Analysis and User Modeling

   1. Mining Temporally Changing Web Usage Graphs

      • Prasanna Desikan, Jaideep Srivastava

      Pages 1-17

   2. Improving the Web Usage Analysis Process: A UML Model of the ETL Process

      • Thilo Maier

      Pages 18-36

3. Web Personalization and Recommender Systems

   1. Mission-Based Navigational Behaviour Modeling for Web Recommender Systems

      • Osmar R. Zaïane, Jia Li, Robert Hayward

      Pages 37-55

   2. Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors

      • Olfa Nasraoui, Mrudula Pavuluri

      Pages 56-72

   3. Collaborative Quality Filtering: Establishing Consensus or Recovering Ground Truth?

      • Jonathan Traupman, Robert Wilensky

      Pages 73-86

4. Search Personalization

   1. Spying Out Accurate User Preferences for Search Engine Adaptation

      • Lin Deng, Wilfred Ng, Xiaoyong Chai, Dik-Lun Lee

      Pages 87-103

   2. Using Hyperlink Features to Personalize Web Search

      • Mehmet S. Aktas, Mehmet A. Nacar, Filippo Menczer

      Pages 104-115

5. Semantic Web Mining

   1. Discovering Links Between Lexical and Surface Features in Questions and Answers

      • Soumen Chakrabarti

      Pages 116-134

   2. Integrating Web Conceptual Modeling and Web Usage Mining

      • Rosa Meo, Pier Luca Lanzi, Maristella Matera, Roberto Esposito

      Pages 135-148

   3. Boosting for Text Classification with Semantic Features

      • Stephan Bloehdorn, Andreas Hotho

      Pages 149-166

   4. Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts

      • Edoardo Airoldi, Xue Bai, Rema Padman

      Pages 167-187

6. Back Matter

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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.

• data mining
• knowledge
• knowledge discovery
• modeling
• recommender system
• semantic web
• web mining

• Center for Web Intelligence School of Computing, DePaul University, Chicago, USA

  Bamshad Mobasher

• Speed School of Engineering, Department of Computer Engineering & Computer Science, University of Louisville, Louisville, USA

  Olfa Nasraoui

• College of Architecture and Urban Planning, Tongji University, Shanghai, P.R. China

  Bing Liu

• Data Miners Inc., Boston, USA

  Brij Masand

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