Clustering and Ranking for Web Information Retrieval

Antonio Gullì

ISBN 10: 3639432967 / ISBN 13: 9783639432961

Published by AV Akademikerverlag Jun 2012, 2012

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Revision with unchanged content. This book investigates several research problems which arise in modern Web Information Retrieval. First of all we consider the fact that there are many situations where a flat list of ten search results are not enough, and that the users might desire to have a larger number of results grouped on-the-fly in folders of similar topics. In this book, we describe Snaket, a hierarchical clustering meta-search engine which personalizes searches according to the clusters selected on-the-fly by users. Second, we consider those situations where users might desire to access fresh information such as news articles. We present a new ranking algorithm suitable for ranking those fresh type of information. Third, we will discuss numerical methodologies for accelerating the ranking methodologies used in Web Search. An important achievement for this book is that we show how to address the above predominant issues of Web Information Retrieval by using clustering and ranking methodologies. We demonstrate that both clustering and ranking have a mutual reinforcement property that has not yet been studied intensively. 144 pp. Englisch.

Seller Inventory # 9783639432961

Bibliographic Details

• Title: Clustering and Ranking for Web Information Retrieval
• Publisher: AV Akademikerverlag Jun 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3639432967
• ISBN 13: 9783639432961
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 233 grams

About this title

Synopsis

Revision with unchanged content. This book investigates several research problems which arise in modern Web Information Retrieval. First of all we consider the fact that there are many situations where a flat list of ten search results are not enough, and that the users might desire to have a larger number of results grouped on-the-fly in folders of similar topics. In this book, we describe Snaket, a hierarchical clustering meta-search engine which personalizes searches according to the clusters selected on-the-fly by users. Second, we consider those situations where users might desire to access fresh information such as news articles. We present a new ranking algorithm suitable for ranking those fresh type of information. Third, we will discuss numerical methodologies for accelerating the ranking methodologies used in Web Search. An important achievement for this book is that we show how to address the above predominant issues of Web Information Retrieval by using clustering and ranking methodologies. We demonstrate that both clustering and ranking have a mutual reinforcement property that has not yet been studied intensively.

About the Author

is the Director of Advanced Search Projects at Ask.com. He holds a Degree in Computer Science, a Degree in Engineering, and a Ph.D. in Computer Science. His research is manly focused in Web Search, Ranking and Clustering. He served as PC Member of many International Conferences such as WWW2008, WWW07, WSDM08, SIGIR07, etc.