Personalized Information Retrieval and Access: Concepts, Methods and Practices (Premier Reference Source)

371 Pages·2008·3.7 MB·English

by  Rafael Andres Gonzalez Rivera

Personalized Information
Retrieval and Access:
Concepts, Methods, and
Practices
Rafael Andrés González(cid:3)
Delft University of Technology, The Netherlands
Nong Chen
Delft University of Technology, The Netherlands
Ajantha Dahanayake
Georgia College & State University, USA
INFORMATION SCIENCE REFERENCE
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Library of Congress Cataloging-in-Publication Data
Personalized information retrieval and access : concepts, methods and practices / Rafael Andres Gonzalez Rivera, Nong Chen, and Ajantha
Dahanayake, editors.
p. cm.
Summary: "This book surveys the main concepts, methods, and practices of personalized information retrieval and access in today's data
intensive, dynamic, and distributed environment, and provides students, researchers, and practitioners with authoritative coverage of recent
technological advances that are shaping the future of globally distributed information retrieval and anywhere, anytime information access"--
Provided by publisher.
Includes bibliographical references and index.
ISBN-13: 978-1-59904-510-8 (hbk.)
ISBN-13: 978-1-59904-512-2 (ebook)
1. Database searching. 2. Information retrieval. 3. Web services. I. Gonzales Rivera, Rafael Andres. II. Chen, Nong, 1976- III.
Dahanayake, Ajantha, 1954-
QA76.9.D3P495123 2008
025.5'24--dc22
2007036852
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book set is original material. The views expressed in this book are those of the authors, but not necessarily of
the publisher.
If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating
the library's complimentary electronic access to this publication.
Table of Contents
Preface .................................................................................................................................................xii
Acknowledgment ................................................................................................................................xx
Section I
Concepts
Chapter I
Learning Personalized Ontologies from Text: A Review on an Inherently Transdisciplinary Area ......1
...... Shan.Chen,.University.of.Technology,.Sydney,.Australia
. Mary-Anne.Williams,.University.of.Technology,.Sydney,.Australia
Chapter II
Overview of Design Options for Neighborhood-Based Collaborative Filterning Systems ..................30
. Nikos.Manouselis,.Informatics.Laboratory,.Agricultural.University.of.Athens,.Greece
. Constantina.Costopoulou,..Informatics.Laboratory,.Agricultural.University.of.Athens,.Greece
Chapter III
Exploring Information Management Problems in the Domain of Critical Incidents ...........................55
. Rafael.Andrés.Gonzalez,.Delft.University.of.Technology,.The.Netherlands
Chapter IV
Mining for Web Personalization ...........................................................................................................77
. Penelope.Markellou,.University.of.Patras,.Greece
. Maria.Rigou,.University.of.Patras,.Greece
. Spiros.Sirmakessis,.University.of.Patras,.Greece
Chapter V
Clustering Web Information Sources.....................................................................................................98
Athena.Vakali,.Aristotle.University.of.Thessaloniki,.Greece
. George.Pallis,.Aristotle.University.of.Thessaloniki,.Greece
. Lefteris.Angelis,.Aristotle.University.of.Thessaloniki,.Greece.
Section II
Methods and Practices
Chapter VI
A Conceptual Structure for Designing Personalized Information Seeking and Retrieval Systems
in Data Intensive Domains ..................................................................................................................119
Nong Chen, Delft University of Technology, The Netherlands
Ajantha Dahanayake, Georgia College & State University, USA
Chapter VII
Privacy Control Requirements for Context-Aware Mobile Services .................................................151
Amr Ali Eldin, Accenture BV, The Netherlands
Zoran Stojanovic, IBM Nederland BV, The Netherlands
Chapter VIII
User and Context-Aware Quality Filters Based on Web Metadata Retrieval .....................................167
Ricardo Barros, Federal University of Rio de Janeiro, Brazil
Geraldo Xexéo, Federal University of Rio de Janeiro, Brazil
Wallace A. Pinheiro, Federal University of Rio de Janeiro, Brazil
Jano de Souza, Federal University of Rio de Janeiro, Brazil
Chapter IX
Personalized Content-Based Image Retrieval ....................................................................................194
Iker Gondra, St. Francis Xavier University, Canada

Chapter X
Service-Oriented Architectures for Context-Aware Information Retrieval and Access ....................220
Lu Yan, University College London, UK
Chapter XI
On Personalizing Web Services Using Context ..................................................................................232
Zakaria Maamar, Zayed University, UAE
Soraya Kouadri Mostéefaoui, Fribourg University, Switzerland
Qusay H. Mahmoud, Guelph University, Canada
Chapter XII
Role-Based Multi-Agent Systems .......................................................................................................254
Haibin Zhu, Nipissing University, Canada
MengChu Zhou, New Jersey Institute of Technology, USA
Chapter XIII
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Tarek Ben Mena, RIADI-ENSI, Tunisia & GRIC-IRIT, France
Narjès Bellamine-Ben Saoud, RIADI-ENSI, Tunisia
Mohamed Ben Ahmed, RIADI-ENSI, Tunisia
Bernard Pavard, GRIC-IRIT, France
Compilation of References ..............................................................................................................308
About the Contributors ...................................................................................................................342
Index ................................................................................................................................................347
Detailed Table of Contents
Preface .................................................................................................................................................xii
Acknowledgment ................................................................................................................................xx
Section I
Concepts
Chapter I
Learning Personalized Ontologies from Text: A Review on an Inherently Transdisciplinary Area ......1
...... Shan.Chen,.University.of.Technology,.Sydney,.Australia
. Mary-Anne.Williams,.University.of.Technology,.Sydney,.Australia
......
Ontology learning has been identified as an inherently transdisciplinary area. Personalized ontology
learning for Web personalization involves Web technologies and therefore presents more challenges.
This chapter presents a review of the main concepts of ontologies and the state of the art in the area of
ontology learning from text. It provides an overview of Web personalization, and identifies issues and
describes approaches for learning personalized ontologies. The goal of this survey is—through the study
of the main concepts, existing methods, and practices of the area—to identify new connections with other
areas for the future success of establishing principles for this new transdisciplinary area. As a result, the
chapter is concluded by presenting a number of possible future research directions.
Chapter II
Overview of Design Options for Neighborhood-Based Collaborative Filterning Systems ..................30
. Nikos.Manouselis,.Informatics.Laboratory,.Agricultural.University.of.Athens,.Greece
. Constantina.Costopoulou,..Informatics.Laboratory,.Agricultural.University.of.Athens,.Greece
The problem of collaborative filtering is to predict how well a user will like an item that he or she has
not rated, given a set of historical ratings for this and other items from a community of users. A plethora
of collaborative filtering algorithms have been proposed in related literature. One of the most prevalent
families of collaborative filtering algorithms are neighborhood-based ones, which calculate a predic-
tion of how much a user will like a particular item, based on how other users with similar preferences
have rated this item. This chapter aims to provide an overview of various proposed design options for
neighborhood-based collaborative filtering systems, in order to facilitate their better understanding, as
well as their study and implementation by recommender systems’ researchers and developers. For this
purpose, the chapter extends a series of design stages of neighborhood-based algorithms, as they have
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alternatives for each design stage and provides an overview of potential design options.
Chapter III
Exploring Information Management Problems in the Domain of Critical Incidents ..........................55
Rafael Andrés Gonzalez, Delft University of Technology, The Netherlands
In this chapter, information management problems and some of the computer-based solutions offered
to deal with them are presented. The claim is that exploring the information problem as a three-fold is-
sue, composed of heterogeneity, overload, and dynamics, will contribute to an improved understanding
of information management problems. On the other hand, it presents a set of computer-based solutions
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information fusion, and information personalization. In addition, this chapter argues that a rich and in-
teresting domain for exploring information management problems is critical incident management, due
to its complexity, requirements, and the nature of the information it deals with.
Chapter IV
Mining for Web Personalization ..........................................................................................................77
Penelope Markellou, University of Patras, Greece
Maria Rigou, University of Patras, Greece
Spiros Sirmakessis, University of Patras, Greece
The Web has become a huge repository of information and keeps growing exponentially under no edito-
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people with access to information is not the problem; the problem is that people with varying needs and
preferences navigate through large Web structures, missing the goal of their inquiry. Web personalization
is one of the most promising approaches for alleviating this information overload, providing tailored Web
experiences. This chapter explores the different faces of personalization, traces back its roots, and fol-
lows its progress. It describes the modules typically comprising a personalization process, demonstrates
its close relation to Web mining, depicts the technical issues that arise, recommends solutions when
possible, and discusses the effectiveness of personalization and related concerns. Moreover, the chapter
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Chapter V
Clustering Web Information Sources ....................................................................................................98
Athena Vakali, Aristotle University of Thessaloniki, Greece
George Pallis, Aristotle University of Thessaloniki, Greece
Lefteris Angelis, Aristotle University of Thessaloniki, Greece
The explosive growth of the Web scale has drastically increased information circulation and dissemina-
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management issues, such as clustering on the Web, should be addressed and analyzed. Clustering has
been proposed towards improving both the information availability and the Web users’ personalization.
Clusters on the Web are either users’ sessions or Web information sources, which are managed in a
variation of applications and implementations testbeds. This chapter focuses on the topic of clustering
information over the Web, in an effort to overview and survey the theoretical background and the adopted
practices of most popular emerging and challenging clustering research efforts. An up-to-date survey
of the existing clustering schemes is given, to be of use for both researchers and practitioners interested
in the area of Web data mining.
Section II
Methods and Practices
Chapter VI
A Conceptual Structure for Designing Personalized Information Seeking and Retrieval Systems
in Data Intensive Domains ..................................................................................................................119
Nong Chen, Delft University of Technology, The Netherlands
Ajantha Dahanayake, Georgia College & State University, USA
Personalized information seeking and retrieval is regarded as the solution to the problem of informa-
tion overload in domains such as crisis response and medical networks. Personalization algorithms and
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dealing with ever-changing user information needs in data-intensive, dynamic, and distributed environ-
ments. In this chapter, we present a conceptual structure for designing personalized, multidisciplinary
information seeking and retrieval systems. This conceptual structure is capable of serving as a bridge
between information needs coming from an organizational process, and existing implementations of
information access services, software, applications, and technical infrastructure; it is also capable of
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new way of thinking about the retrieval of personalized information.
Chapter VII
Privacy Control Requirements for Context-Aware Mobile Services .................................................151
Amr Ali Eldin, Accenture BV, The Netherlands
Zoran Stojanovic, IBM Nederland BV, The Netherlands
With the rapid developments of mobile telecommunications technology over the last two decades, a new
computing paradigm known as ‘anywhere and anytime’ or ‘ubiquitous’ computing has evolved. Conse-
quently, attention has been given not only to extending current Web services and mobile service models
and architectures, but increasingly also to make these services context-aware. Privacy represents one of
the hot topics that has questioned the success of these services. In this chapter, we discuss the different
requirements of privacy control in context-aware service architectures. Further, we present the different
functionalities needed to facilitate this control. The main objective of this control is to help end users
make consent decisions regarding their private information collection under conditions of uncertainty.
The proposed functionalities have been prototyped and integrated in a UMTS location-based mobile
services testbed platform on a university campus. Users have experienced the services in real time. A
survey of users’ responses on the privacy functionality has been carried out and analyzed as well. Users’
collected response on the privacy functionality was positive in most cases. Additionally, results obtained
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Chapter VIII
User and Context-Aware Quality Filters Based on Web Metadata Retrieval .....................................167
Ricardo Barros, Federal University of Rio de Janeiro, Brazil
Geraldo Xexéo, Federal University of Rio de Janeiro, Brazil
Wallace A. Pinheiro, Federal University of Rio de Janeiro, Brazil
Jano de Souza, Federal University of Rio de Janeiro, Brazil
This chapter addresses the issues regarding the large amount and low quality of Web information by
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retrieval. This starts with an initial evaluation and adjusts it to consider context characteristics and user
perspectives to obtain aggregated evaluation values.
Chapter IX
Personalized Content-Based Image Retrieval ....................................................................................194
Iker Gondra, St. Francis Xavier University, Canada
In content-based image retrieval (CBIR), a set of low-level features are extracted from an image to
represent its visual content. Retrieval is performed by image example, where a query image is given
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responding feature space. This approach suffers from the fact that there is a large discrepancy between
the low-level visual features that one can extract from an image and the semantic interpretation of the
image’s content that a particular user may have in a given situation. That is, users seek semantic similar-
ity, but we can only provide similarity based on low-level visual features extracted from the raw pixel
data, a situation known as the semantic gap. The selection of an appropriate similarity measure is thus
an important problem. Since visual content can be represented by different attributes, the combination
and importance of each set of features varies according to the user’s semantic intent. Thus, the retrieval
strategy should be adaptive so that it can accommodate the preferences of different users.

Chapter X
Service-Oriented Architectures for Context-Aware Information Retrieval and Access ....................220
Lu Yan, University College London, UK
Humans are quite successful at conveying ideas to each other and retrieving information from interac-
tions appropriately. This is due to many factors: the richness of the language they share, the common
understanding of how the world works, and an implicit understanding of everyday situations. When
humans talk with humans, they are able to use implicit situational information (i.e., context) to enhance
the information exchange process. Context plays a vital part in adaptive and personalized information
retrieval and access. Unfortunately, computer communications lacks this ability to provide auxiliary
context in addition to the substantial content of information. As computers are becoming more and more
ubiquitous and mobile, there is a need and possibility to provide information “personalized, any time,