Clustering and Ranking for Web Information Retrieval: Methodologies for Searching the Web - Couverture souple

Gullì, Antonio

 
9783639432961: Clustering and Ranking for Web Information Retrieval: Methodologies for Searching the Web
Couverture souple
ISBN 10 : 3639432967 ISBN 13 : 9783639432961
Editeur : AV Akademikerverlag, 2012

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
AV Akademikerverlag
Date d'édition
2012
Langue
anglais
ISBN 10 
3639432967
ISBN 13 
9783639432961
Reliure
Broché
Nombre de pages
144
Coordonnées du fabricant
non disponible
Personne responsable
non disponible

Autres éditions populaires du même titre

9783836456579: Clustering and Ranking for Web Information Retrieval

Edition présentée

ISBN 10 :  3836456575 ISBN 13 :  9783836456579
Editeur : VDM Verlag Dr. Mueller E.K., 2008
Couverture souple