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Author Velásquez, J.D
Title Adaptive Web Sites : A Knowledge Extraction from Web Data Approach
Imprint Amsterdam : IOS Press, 2008
©2008
book jacket
Descript 1 online resource (296 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Frontiers in Artificial Intelligence and Applications
Frontiers in Artificial Intelligence and Applications
Note Title page -- Contents -- Foreword -- Introduction -- The World Wide Web -- Towards new portal generation -- Structure of the book -- Web data -- Web's Operation -- The information behind the clicks -- Session reconstruction process -- Finding real sessions -- The information contained in a web page -- Web page content -- Web page links -- Summary -- Knowledge discovery from web data -- Overview -- Data sources and cleaning -- Data consolidation and information repositories -- Data Mining -- Motivation -- Data Mining techniques -- Association rules -- Classification -- Clustering -- Tools for mining data -- Artificial Neural Networks (ANN) -- Self-Organizing Feature Maps (SOFMs) -- K-means -- Decisions trees -- Bayesian networks -- K-Nearest Neighbor (KNN) -- Support vector machines (SVMs) -- Using data mining to extract knowledge -- Validation of the extracted knowledge -- Mining the web -- Summary -- Web information repository -- A short history of data storage -- Storing historical data -- Information systems -- Data Mart and Data Warehouse -- The multidimensional analysis -- The Cube Model -- The Star Model -- The Extraction, Transformation and Loading Process -- Extraction -- Transformation -- Loading -- Web warehousing -- Information repository for web data -- Thinking the web data in several dimensions -- Hyper cube model for storing web data -- Star model for storing web data -- Selecting a model for maintaining web data -- ETL process applied to web data -- Processing web page text content -- Processing the inner web site hyperlinks structure -- Processing the web logs -- Summary -- Mining the Web -- Mining the structure -- The HITS algorithm -- The Page Rank algorithm -- Identifying web communities -- Mining the content -- Classification of web page text content -- Clustering for groups having similar web page text content
Some applications -- WEBSOM -- Automatic web page text summarization -- Extraction of key-text components from web pages -- Mining the usage data -- Statistical methods -- Clustering the user sessions -- Classification of the user behavior in a web site -- Using association rules for discovering navigation patterns -- Using sequence patterns for discovering common access paths -- Some particular implementations -- Web query mining -- Prefetching and caching -- Helping the user's navigation in a web site -- Improving the web site structure and content -- Web-based adaptive systems -- Summary -- Web-based personalization systems -- Recommendation Systems -- Short historical review -- Web-based recommender systems -- Web recommender systems, particular approaches and examples -- Systems for personalization -- Computerized personalization -- Effectiveness of computerized personalization systems -- Computerized personalization approaches -- Web personalization -- Aspects of web personalization privacy -- Main approaches for web personalization -- Privacy aspects of web personalization privacy -- Adaptive web-based systems -- A short introduction -- Elements to take into account -- Web site changes and recommendations -- Adaptive systems for web sites -- Summary -- Extracting patterns from user behavior in a web site -- Modelling the web user behavior -- Web data preparation process -- Comparing web page contents -- Comparing the user navigation sequences -- Extracting user browsing preferences -- Comparing user browsing behavior -- Applying a clustering algorithm for extracting navigation patterns -- Extracting user web page content preferences -- Comparing user text preferences -- Identifying web site keywords -- Summary -- Acquiring and maintaining knowledge extracted from web data -- Knowledge Representation
Fundamental roles of knowledge representation -- Rules -- Knowledge repository -- Representing and maintaining knowledge -- Knowledge web users -- A framework to maintain knowledge extracted from web data -- Overview -- The Web Information Repository -- The Knowledge Base -- Pattern Repository -- Rule Repository -- Integration with adaptive web sites -- Summary -- A framework for developing adaptive web sites -- The adaptive web site proposal -- Selecting web data -- Extracting information from web data -- The star model used for the creation of the WIR -- Session reconstruction process -- Web page content preprocessing -- Applying web mining techniques -- Analyzing the user browsing behavior -- Applying statistics -- Using SOFM for extracting navigation patterns -- Using K-means for extracting navigation patterns -- Analyzing user text preferences -- Using the extracted knowledge for creating recommendations -- Offline recommendations -- Structure recommendations -- Content recommendations -- Online recommendations -- Testing the recommendation effectiveness -- Testing offline structure recommendation -- Testing offline content recommendation -- Testing online navigation recommendation -- Storing the extracted knowledge -- Pattern Repository -- Rules for navigation recommendations -- Summary -- In place of conclusions -- Bibliography
This book can be presented in two different ways; introducing a particular methodology to build adaptive Web sites and; presenting the main concepts behind Web mining and then applying them to adaptive Web sites. In this case, adaptive Web sites is the case study to exemplify the tools introduced in the text. The authors start by introducing the Web and motivating the need for adaptive Web sites. The second chapter introduces the main concepts behind a Web site: its operation, its associated data and structure, user sessions, etc. Chapter three explains the Web mining process and the tools to analyze Web data, mainly focused in machine learning.The fourth chapter looks at how to store and manage data. Chapter five looks at the three main and different mining tasks: content, links and usage. The following chapter covers Web personalization, a crucial topic if we want to adapt our site to specific groups of people. Chapter seven shows how to use information extraction techniques to find user behavior patterns. The subsequent chapter explains how to acquire and maintain knowledge extracted from the previous phase. Finally, chapter nine contains the case study where all the previous concepts are applied to present a framework to build adaptive Web sites. In other words, the authors have taken care of writing a self-contained book for people that want to learn and apply personalization and adaptation in Web sites. This is commendable considering the large and increasing bibliography in these and related topics. The writing is easy to follow and although the coverage is not exhaustive, the main concepts and topics are all covered
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Velásquez, J.D. Adaptive Web Sites : A Knowledge Extraction from Web Data Approach Amsterdam : IOS Press,c2008 9781586038311
Subject Adaptive computing systems.;Data mining.;Web databases
Electronic books
Alt Author Palade, V
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