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.IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences
Author(s): J. D. Velasquez, V. Palade
Series: Frontiers in Artificial Intelligence and Applications 170
Publisher: IOS Press
Year: 2008
Language: English
Pages: 296
Title page......Page 1
Contents......Page 7
Foreword......Page 23
Introduction......Page 25
The World Wide Web......Page 26
Towards new portal generation......Page 29
Structure of the book......Page 31
Web data......Page 33
Web's Operation......Page 34
The information behind the clicks......Page 37
Session reconstruction process......Page 39
Finding real sessions......Page 42
Web page content......Page 43
Web page links......Page 45
Summary......Page 47
Knowledge discovery from web data......Page 49
Overview......Page 50
Data sources and cleaning......Page 52
Data consolidation and information repositories......Page 54
Motivation......Page 56
Association rules......Page 57
Clustering......Page 58
Artificial Neural Networks (ANN)......Page 61
Self-Organizing Feature Maps (SOFMs)......Page 65
K-means......Page 67
Decisions trees......Page 68
Bayesian networks......Page 70
K-Nearest Neighbor (KNN)......Page 72
Support vector machines (SVMs)......Page 74
Using data mining to extract knowledge......Page 77
Mining the web......Page 79
Summary......Page 80
Web information repository......Page 83
A short history of data storage......Page 84
Storing historical data......Page 86
Information systems......Page 87
Data Mart and Data Warehouse......Page 89
The multidimensional analysis......Page 91
The Cube Model......Page 94
The Star Model......Page 96
Extraction......Page 99
Transformation......Page 100
Web warehousing......Page 101
Information repository for web data......Page 103
Thinking the web data in several dimensions......Page 104
Hyper cube model for storing web data......Page 106
Star model for storing web data......Page 108
Selecting a model for maintaining web data......Page 109
ETL process applied to web data......Page 110
Processing the inner web site hyperlinks structure......Page 111
Processing the web logs......Page 112
Summary......Page 114
Mining the Web......Page 117
Mining the structure......Page 118
The HITS algorithm......Page 119
The Page Rank algorithm......Page 122
Identifying web communities......Page 125
Mining the content......Page 126
Classification of web page text content......Page 127
Clustering for groups having similar web page text content......Page 129
WEBSOM......Page 130
Automatic web page text summarization......Page 131
Extraction of key-text components from web pages......Page 132
Mining the usage data......Page 133
Clustering the user sessions......Page 134
Classification of the user behavior in a web site......Page 136
Using association rules for discovering navigation patterns......Page 137
Using sequence patterns for discovering common access paths......Page 138
Web query mining......Page 139
Prefetching and caching......Page 140
Helping the user's navigation in a web site......Page 142
Improving the web site structure and content......Page 143
Web-based adaptive systems......Page 144
Summary......Page 145
Web-based personalization systems......Page 149
Recommendation Systems......Page 150
Short historical review......Page 151
Web-based recommender systems......Page 153
Web recommender systems, particular approaches and examples......Page 156
Systems for personalization......Page 157
Computerized personalization......Page 158
Effectiveness of computerized personalization systems......Page 160
Computerized personalization approaches......Page 161
Web personalization......Page 163
Aspects of web personalization privacy......Page 165
Main approaches for web personalization......Page 167
Privacy aspects of web personalization privacy......Page 168
Adaptive web-based systems......Page 171
A short introduction......Page 172
Elements to take into account......Page 174
Web site changes and recommendations......Page 175
Adaptive systems for web sites......Page 177
Summary......Page 178
Extracting patterns from user behavior in a web site......Page 181
Modelling the web user behavior......Page 182
Web data preparation process......Page 186
Comparing web page contents......Page 187
Comparing the user navigation sequences......Page 190
Comparing user browsing behavior......Page 193
Applying a clustering algorithm for extracting navigation patterns......Page 194
Extracting user web page content preferences......Page 197
Comparing user text preferences......Page 198
Identifying web site keywords......Page 199
Summary......Page 201
Acquiring and maintaining knowledge extracted from web data......Page 203
Fundamental roles of knowledge representation......Page 204
Rules......Page 206
Representing and maintaining knowledge......Page 207
Knowledge web users......Page 209
Overview......Page 210
The Web Information Repository......Page 212
The Knowledge Base......Page 213
Pattern Repository......Page 214
Rule Repository......Page 215
Integration with adaptive web sites......Page 217
Summary......Page 218
A framework for developing adaptive web sites......Page 219
The adaptive web site proposal......Page 220
Selecting web data......Page 221
Extracting information from web data......Page 224
Session reconstruction process......Page 225
Web page content preprocessing......Page 230
Applying statistics......Page 232
Using SOFM for extracting navigation patterns......Page 233
Using K-means for extracting navigation patterns......Page 236
Analyzing user text preferences......Page 237
Structure recommendations......Page 241
Content recommendations......Page 243
Testing the recommendation effectiveness......Page 244
Testing offline structure recommendation......Page 245
Testing offline content recommendation......Page 249
Testing online navigation recommendation......Page 250
Storing the extracted knowledge......Page 253
Pattern Repository......Page 254
Rules for navigation recommendations......Page 255
Summary......Page 257
In place of conclusions......Page 261
Bibliography......Page 265
