Search

This book brings together results from the Web search studies we conducted from 1997 through 2004. The aim of our studies has been twofold: to examine how the public at large searches the Web and to highlight trends in public Web searching. The eight-year period from 1997 to 2004 saw the beginnings and maturity of public Web searching. Commercial Web search engines have come and gone, or endured, through the fall of the dot.com companies. We saw the rise and, in some cases, the demise of several high profile, publicly available Web search engines. The study of the Web search is an exciting and important area of interdisciplinary research. Our book provides a valuable insight into the growth and development of human interaction with Web search engines. In this book, our focus is on the human aspect of the interaction between user and Web search engine. We do not investigate the Web search engines themselves or their constantly changing interfaces, algorithms and features. We focus on exploring the cognitive and user aspects of public Web searching in the aggregate. We use a variety of quantitative and qualitative methods within the overall methodology known as transaction log analysis.

After the advent of data mining and its successful application on conventional data, Web-related information has been an appropriate and increasingly popular target of knowledge discovery. Depending on whether the data used in the knowledge discovery process concerns the Web itself in terms of content or the usage of the content, one distinguishes between Web content mining and Web usage mining.This book is the first one entirely devoted to Web usage mining. It originates from the WEBKDD'99 Workshop held during the 1999 KDD Conference. The ten revised full papers presented together with an introductory survey by the volume editors documents the state of the art in this exciting new area. The book presents topical sections on Modeling the User, Discovering Rules and Patterns of Navigation, and Measuring interestingness in Web Usage Mining.