IGI global personalized information retrieval and access concepts methods and practices apr 2008 ISBN 1599045109 pdf

Personalized Information
Retrieval and Access:
Concepts, Methods, and
Practices
Rafael Andrés González
Delft University of Technology, The Netherlands
Nong Chen
Delft University of Technology, The Netherlands
Ajantha Dahanayake
Georgia College & State University, USA

INFORMATION SCIENCE REFERENCE
Hershey • New York


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not indicate a claim of ownership by IGI Global of the trademark or registered trademark.
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, 1954QA76.9.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 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
7RZDUGVD&RQWH[W'H¿QLWLRQIRU0XOWL$JHQW6\VWHPV ................................................................... 286
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 prediction 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 issue, 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 interesting 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 editoULDOFRQWUROZKLOHWKHKXPDQFDSDELOLW\WR¿QGUHDGDQGXQGHUVWDQGFRQWHQWUHPDLQVFRQVWDQW3URYLGLQJ
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 follows 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 disseminaWLRQUDWHV$VWKHQXPEHURIERWK:HEXVHUVDQG:HEVRXUFHVJURZVVLJQL¿FDQWO\HYHU\GD\FUXFLDOGDWD
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 information 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 environments. 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
VXI¿FLHQWO\GHVFULELQJDQGLQIHUULQJXVHUV¶SHUVRQDOL]HGLQIRUPDWLRQQHHGV:HEHOLHYHWKDWLWRIIHUVD
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. Consequently, 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
UHÀHFWHGWKHIHDVLELOLW\DQGXVDELOLW\RIWKLVDSSURDFK
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
DVLQSXWE\WKHXVHUDQGDQDSSURSULDWHVLPLODULW\PHDVXUHLVXVHGWR¿QGWKHEHVWPDWFKHVLQWKHFRUresponding 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 similarity, 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 interactions 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,


and anywhere.” In these scenarios, large amounts of information circulate in order to create smart and
SURDFWLYHHQYLURQPHQWVWKDWZLOOVLJQL¿FDQWO\HQKDQFHERWKWKHZRUNDQGOHLVXUHH[SHULHQFHVRISHRSOH
Context-awareness plays an important role in enabling personalized information retrieval and access
according to the current situation with minimal human intervention. Although context-aware information retrieval systems have been researched for a decade, the rise of mobile and ubiquitous computing
put new challenges to issue, and therefore we are motivated to come up with new solutions to achieve
non-intrusive, personalized information access on the mobile service platforms and heterogeneous wireless environments.
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
This chapter presents a context-based approach for Web services personalization so that user preferences
are accommodated. Preferences are of different types varying from when the execution of a Web service
should start to where the outcome of this execution should be delivered according to user location. Besides user preferences, this chapter will discuss that the computing resources on which the Web services
operate have an impact on their personalization. Indeed resources schedule the execution requests that
originate from multiple Web services. To track the personalization of a Web service from a temporal
perspective (i.e., what did happen, what is happening, and what will happen), three types of contexts are
devised and referred to as user context, Web service context, and resource context.

Chapter XII
Role-Based Multi-Agent Systems....................................................................................................... 254
Haibin Zhu, Nipissing University, Canada
MengChu Zhou, New Jersey Institute of Technology, USA
In this chapter, the authors introduce roles as a means to support interaction and collaboration among
DJHQWVLQPXOWLDJHQWV\VWHPV7KH\UHYLHZWKHDSSOLFDWLRQRIUROHVLQFXUUHQWDJHQWV\VWHPVDW¿UVWWKHQ
describe the fundamental principles of role-based collaboration and propose the basic methodologies of
how to apply roles into agent systems (i.e., the revised E-CARGO model). After that, they demonstrate
DFDVHVWXG\DVRFFHUURERWWHDPGHVLJQHGZLWKUROHVSHFL¿FDWLRQV)LQDOO\WKHDXWKRUVSUHVHQWWKHSRtentiality to apply roles into information personalization.
Chapter XIII
7RZDUGVD&RQWH[W'H¿QLWLRQIRU0XOWL$JHQW6\VWHPV ................................................................... 286
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
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the art on context in different disciplines, the authors present context as a generic and abstract notion.


They argue that context depends on three characteristics: domain, entity, and problem. By specifying
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components—actant, role, and situation—and then from an intensional one, which represents the context model for agents in MAS which consist of information on environment, other objects, agents, and
relations between them. Therefore, they underline a new way of representing agent knowledge, building
context on this knowledge, and using it. Furthermore, the authors prove the applicability of contextual
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account as agents: crawlers and as objects: documents.

Compilation of References .............................................................................................................. 308
About the Contributors ................................................................................................................... 342

Index ................................................................................................................................................ 347


xii

Preface

The existence of large volumes of globally distributed information and the availability of various
computing devices, many of which are mobile, present the possibility of anywhere-anytime access to
information. This enables individuals and organizations to coordinate and improve their knowledge over
various autonomous locations. However, the amount and nature of information can result in overload
problems, in heterogeneity of formats and sources, in rapidly changing content, and in uncertain user
LQIRUPDWLRQQHHGV,QGLYLGXDOVDQGRUJDQL]DWLRQVPD\WKXVEHIDFHGZLWKLQFUHDVLQJGLI¿FXOW\LQ¿QGLQJ
the “right information” in the “right format” at the “right time.”
In an already classic paper, Imielinski and Badrinath (1994) presented the trends and challenges surrounding mobile computing, which they said held the promise of access to information “anywhere and
at any time.” The idea was that mobile or nomadic computing was possible thanks to mobile computers
having access to wireless connections to information networks, resulting in more collaborative forms of
computing. What Imielinski and Badrinath presented as challenges continue to be critical issues in the
development of mobile applications and information services today. They pointed at heterogeneity as a
UHVXOWRIWKHPDVVLYHVFDOHRIPRELOHHQYLURQPHQWVWKH\PHQWLRQHGWKHQHHGIRUG\QDPLFUHFRQ¿JXUDWLRQ
of services in response to client mobility, and they reminded us of the privacy and security implications
of mobility. Consequently, they argued that mobility would have far-reaching consequences for systems
GHVLJQDQGLQGHHGWKH\ZHUHULJKW7KLVERRN¿QGVPRWLYDWLRQRQWKRVHLVVXHVIRFXVLQJRQWKHVXEMHFW
of information retrieval and accesspersonalization in particular.
Chapters IV, VII, and X of this book explicitly address mobility challenges and propose ways to
deal with them. Mobility is currently tied, from a telecommunications perspective, with next-generation wireless technologies that promise ubiquitous networking and mobile computing on a large scale,
providing high-bandwidth data services and wireless Internet (Pierre, 2001). This can be grouped under
the term “mobile next-generation networks (NGNs)” (Huber, 2004), which refers to the convergence of
the Internet and intranets with mobile networks and with media and broadcasting technologies (Universal
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access services, normally accessible in a wired manner, from anywhere (Pierre, 2001). Mobile computing
uses such mobility to allow users of portable devices to access information services through a shared
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the following types:
•

Terminal mobility: The ability to locate and identify mobile terminals as they move, to allow
them access to telecommunication services (Pierre, 2001).


xiii

•

•

Personal mobility: Centers around users carrying a personal unique subscription identity and the
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Bochman, 2004; Pierre, 2001).
Service mobility: The capacity of a network to provide subscribed services at the terminal or location determined by users (Pierre, 2001); this allows the possibility of suspending a service and
resuming it on another device (El-Khatib et al., 2004).

Ubiquitous computing, for some the next wave after the “Internet wave,” uses the advances in mobile computing and integrates them with pervasive computing, which refers to the acquiring of context
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computing paradigm has the goal of embedding small and highly specialized devices within day-to-day
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WRRIÀLQHRURQOLQH
users (Singh et al., 2006; Huber, 2004). Ubiquitous computing integrates several technologies, which
include embedded systems, service discovery, wireless networking, and personal computing (El-Khatib
et al., 2004).
Research in ubiquitous computing has shown three main focuses: (1) how to provide users with
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with context-awareness ability to adapt the service behaviors or device behaviors according to various
situations, or (3) a combination of the above. Therefore, personalization and context-awareness are of
special importance for the development of ubiquitous computing.
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3HUVRQDOL]DWLRQLQWKHFRQWH[WRIXELTXLWRXVFRPSXWLQJLVJHQHUDOO\
meant to denote the ability to customize the user interface, the information content, the information
channels, and the services provided according to the individual user’s needs, personal interests, and
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$GGLQJSHUVRQDOL]HGIXQFWLRQVLQWR,QWHUQHWHQDEOHGLQIRUmation retrieval and access applicationsfor example, search engines or e-servicesis becoming one
of the competitive advantages used to attract users to survive in the current competitive business world.
There are several personalization strategies, such as interface personalization, link personalization,
content personalization, and context personalization. Personalization models, methods, and techniques
built based on solid mathematic foundations and advanced programming languages are studied in the
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overload at the technological level, ranging from simple user-controlled information personalization to
autonomous system-controlled adaptation.
Context-awareness is the second important issue of mobile and ubiquitous computing, because this
type of computing requires sharing knowledge between individual environments and providing services that take the environmental characteristics and constraints into account. A human user is typically
associated with many environments and consequently adopts different roles in each one; the system
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a piece of information that can be used to characterize the situation of a participant, so by sensing this
context, applications can present contextual information to users or modify their behavior according to

the environmental changes (Singh et al., 2006). A true ubiquitous system should provide the best possible service(s) based on the user role and its associated privileges, restrictions, location, and time. This
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of the following types (El-Khatib et al., 2004):


xiv

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•
•

8VHUSUR¿OH Personal properties and preferences.
&RQWHQWSUR¿OH Metadata about the content, including storage features, available variants, author
and production, and usage (metadata is a topic addressed by Chapter VIII).
&RQWH[WSUR¿OH Dynamic information that is part of the context or status of the user, including
physical, social, and organizational information.
'HYLFHSUR¿OH Hardware and software characteristics of a computing device.
1HWZRUNSUR¿OH Resources and capabilities of the communication network.
,QWHUPHGLDULHVSUR¿OHDescription of all adaptation services that intermediaries can provide.

Context-awareness and personalization are topics treated in Chapters I, III, IV, V, VI, VII, X, XI, and
;,,,RIWKLVERRN$PRQJWKHVSHFL¿FDSSOLFDWLRQVRIFRQWH[WDZDUHQHVVDQGSHUVRQDOL]DWLRQLVFROODERUDWLYH¿OWHULQJDQGUHFRPPHQGDWLRQWUHDWHGLQ&KDSWHUV,,DQG,,,&ROODERUDWLYHUHFRPPHQGDWLRQLV
a personalization technique that keeps track of user preferences and uses them to offer new suggestions
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The idea is to recommend items to a target customer, by looking at customers who have expressed similar preferences. This helps individuals more effectively identify content of interest from a potentially
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