Personalized Information Retrieval and Access: Concepts, Methods, and Practices

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

Hershey • New York

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.

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

Table of Contents

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

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

Detailed Table of Contents

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￾ULDOFRQWUROZKLOHWKHKXPDQFDSDELOLW\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 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￾WLRQUDWHV$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 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

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¿QGWKHEHVWPDWFKHVLQWKHFRU￾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,

and anywhere.” In these scenarios, large amounts of information circulate in order to create smart and

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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 informa￾tion 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 wire￾less 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. Be￾sides 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

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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\WKHDXWKRUVSUHVHQWWKHSR￾tentiality 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 con￾text 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 sur￾rounding 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

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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-genera￾tion 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

0RELOH7HOHFRPPXQLFDWLRQ6\VWHPRU8076DPRQJWKHP0RELOLW\FDQEHGH¿QHGDVWKHDELOLW\WR

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 lo￾cation 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 mo￾bile computing and integrates them with pervasive computing, which refers to the acquiring of context

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7KHUHVXOWLVDJOREDOFRPSXWLQJHQYLURQPHQWWKDWLVGH¿QHGDVXELTXLWRXVFRPSXWLQJ7KLVQRYHO

computing paradigm has the goal of embedding small and highly specialized devices within day-to-day

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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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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

SUHIHUHQFHV+\OGHJDDUG 6HLGHQ$GGLQJSHUVRQDOL]HGIXQFWLRQVLQWR,QWHUQHWHQDEOHGLQIRU￾mation 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 ser￾vices 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 pos￾sible 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

• 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

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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 simi￾lar preferences. This helps individuals more effectively identify content of interest from a potentially

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Some of the recent technologies on which personalized information services, context-awareness,

and ubiquitous computing in general are grounded are: software components, service orientation, and

multi-agent systems. A software component is any coherent design unit which may be packaged, sold,

stored, assigned to a person or team (for development), maintained, and perhaps most importantly, reused

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CBD for short) includes improvements in: quality, throughput, performance, reliability and interoper￾DELOLW\UHGXFLQJGHYHORSPHQWGRFXPHQWDWLRQPDLQWHQDQFHDQGVWDIIWUDLQLQJWLPHDQGFRVW+HU]XP

Sims, 2000; Szyperski, 2002). Most recent trends in software engineering show that future developments

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development technologies that have existed for some years now (CORBA, EJB, DCOM, and .NET,

among others), and also by the increasing amount of components available in the market (Andrews,

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component notion (Apperly et al., 2003). By using interfaces and Web-enabled standards for discovery

and representation, services (e.g., information services) are offered for consumption to different ap￾plications, making service consumption truly aligned with the possibilities of ubiquitous computing. In

addition to components and services, software agents are another technology that can underlie mobile

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(which deals with autonomy and intelligence in agent behavior) and distributed object systems (which

extend with mobility the object-oriented approach) (Marinescu, 2002). As such, an agent can be seen as

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learning capabilities). An agent can also be described in human-like terms of knowledgeknowledge of

itself; knowledge of other agents, goals, or possible solutions; and knowledge of its own desires, com￾mitments, and intentionsDQGFDSDELOLWLHVFRPPXQLFDWLRQDQGUHDVRQLQJ6KDNVKXNL*KHQQLZD

Kamel, 2003). In this book, components and services related to context-awareness and personalization

are treated in Chapters VI, VII, and XI. Agents are treated in several chapters, due to their prominence

in modern software technology. In particular, Chapters III, XII, and XIII mention agent-based solutions

to some of the challenges that will be presented in the next section of this preface.