Intelligent Information Personalization: From Issues to Strategies

Intelligent Information Personalization: From Issues to Strategies

Syed Sibte Raza Abidi (Dalhousie University, Canada)
DOI: 10.4018/978-1-60566-032-5.ch006
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This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based on an individual’s interests, needs and goals. We present intelligent techniques to dynamically compose new personalized information by adapting existing web-based information in line with a dynamic user-model, whilst simultaneously addressing linguistic, factual and functional requirements. This chapter will highlight the different facets, tasks and issues concerning intelligent information personalization to guide researchers in designing intelligent information personalization applications. The chapter presents intelligent methods that address information personalization at the content level as opposed to the traditional approaches that focus on interface level information personalization. To assist researchers in designing intelligent information personalization applications we present our information personalization framework, named AdWISE (Adaptive Webmediated Information and Services Environment), to demonstrate how to systematically integrate various intelligent methods to achieve information personalization. We will conclude with a commentary on the future outlook for intelligent information personalization.
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Introduction: Information Personalization

The access to and consumption of relevant, useful and correct information is paramount to Web users. However, the sheer volume of information available over the Web has led to the much-cited information overload problem; users are finding it cognitively stressful and difficult to find the ‘right’ and ‘relevant’ information. Notwithstanding the efficacy of information retrieval technologies, it is argued that solutions to tackle the information overload problem need to pursue a shift in focus—i.e. move from searching for information guided by the user’s query towards personalizing the available information guided by the user’s immediate needs and interests.

Information retrieval services such as Google, Yahoo, CiteSeer are now the preferred gateways or mediators to the vast information artefacts available over the Web (Shahabi et al. 2003). The term information artifact is used to broadly denote a document, image, media file and any other medium to represent information. Such information artifacts may either be structured, semi-structured, or unstructured. Functionally speaking, such information services aim to address the information overload problem by (a) finding a subset of information artifacts from a larger space of information artifacts (i.e. the Web) based on the user’s search preferences; and (b) presenting a list of relevant information artifacts to the user—the user is required to subsequently choose from the list of retrieved information artifacts. Indeed, this alleviates the information overload problem to some extent but it does not fully solve the cognitive overload problem because the user is still required to filter the retrieved information based on contextual priorities, and then adapt it based on personal preferences.

Information users are different in nature—they manifest heterogeneous information seeking behaviours, needs and expectations. Yet, we note that most information retrieval services purport a one size fits all model whereby the same information is disseminated to a wide range of information users despite the individualistic nature of each user’s needs, goals, interests, preferences, intellectual levels and information consumption capacity. We believe that this leads to a sub-optimal model because information seekers who are intrinsically distinct are not only compelled to experience a generic outcome but are further required to manually adjust and adapt the recommended information artifacts according to their immediate needs or preferences in order to achieve the desired results (Abidi et al, 2004a; Abidi et al, 2006). Therefore, we argue that there is both a case and the need to design information services that take into account the individuality of information seekers, and in turn aim to personalize their information seeking experiences and outcomes (Belkin et al, 1992; Abidi, 2002; Fink et al, 2002; Shahabi et al, 2003; Brusilovsky et al, 2006).

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Editorial Advisory Board
Table of Contents
Barry Smyth
Constantinos Mourlas, Panagiotis Germanakos
Constantinos Mourlas, Panagiotis Germanakos
Chapter 1
Nikos Tsianos, Panagiotis Germanakos, Zacharias Lekkas, Constantinos Mourlas
The plethora of information and services as well as the complicated nature of most Web structures intensify the navigational difficulties that arise... Sample PDF
Assessment of Human Factors in Adaptive Hypermedia Environments
Chapter 2
Barry Smyth
Everyday hundreds of millions of users turn to the World-Wide Web as their primary source of information during their educational, business and... Sample PDF
Case Studies in Adaptive Information Access: Navigation, Search, and Recommendation
Chapter 3
Sherry Y. Chen
Web-based instruction is prevalent in educational settings. However, many issues still remain to be investigated. In particular, it is still open... Sample PDF
The Effects of Human Factors on the Use of Web-Based Instruction
Chapter 4
Gulden Uchyigit
Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization... Sample PDF
The Next Generation of Personalization Techniques
Chapter 5
Nancy Alonistioti
This chapter introduces context-driven personalisation of service provision based on a middleware architectural approach. It describes the emerging... Sample PDF
Advanced Middleware Architectural Aspects for Personalised Leading-Edge Services
Chapter 6
Syed Sibte Raza Abidi
This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based... Sample PDF
Intelligent Information Personalization: From Issues to Strategies
Chapter 7
Babis Magoutas
This chapter introduces a semantically adaptive interface as a means of measuring the quality of egovernment portals, based on user feedback. The... Sample PDF
A Semantically Adaptive Interface for Measuring Portal Quality in E-Government
Chapter 8
Fabio Grandi, Federica Mandreoli, Riccardo Martoglia, Enrico Ronchetti, Maria Rita Scalas
While the World Wide Web user is suffering form the disease caused by information overload, for which personalization is one of the treatments which... Sample PDF
Ontology-Based Personalization of E-Government Services
Chapter 9
Maria Golemati, Costas Vassilakis, Akrivi Katifori, George Lepouras, Constantin Halatsis
Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and... Sample PDF
Context and Adaptivity-Driven Visualization Method Selection
Chapter 10
Honghua Dai
Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional... Sample PDF
Integrating Semantic Knowledge with Web Usage Mining for Personalization
Chapter 11
Constantinos Mourlas
One way to implement adaptive software is to allocate resources dynamically during run-time rather than statically at design time. Design of... Sample PDF
Adaptive Presentation and Scheduling of Media Streams on Parallel Storage Servers
Chapter 12
Gheorghita Ghinea
This study investigated two dimensions of cognitive style, including Verbalizer/Imager and Field Dependent/ Field Independent and their influence on... Sample PDF
Impact of Cognitive Style on User Perception of Dynamic Video Content
Chapter 13
Mathias Bauer, Alexander Kröner, Michael Schneider, Nathalie Basselin
Limitation of the human memory is a well-known issue that anybody has experienced. This chapter discusses typical components and processes involved... Sample PDF
Building Digital Memories for Augmented Cognition and Situated Support
Chapter 14
Rafael Morales, Nicolas Van Labeke, Paul Brna, María Elena Chan
It is believed that, with the help of suitable technology, learners and systems can cooperate in building a sufficiently accurate learner model they... Sample PDF
Open Learner Modelling as the Keystone of the Next Generation of Adaptive Learning Environments
Chapter 15
Klaus Jantke, Christoph Igel, Roberta Sturm
Humans need assistance in learning. This is particularly true when learning is supported by modern information and communication technologies. Most... Sample PDF
From E-Learning Tools to Assistants by Learner Modelling and Adaptive Behavior
Chapter 16
Violeta Damjanovic, Milos Kravcik
The process of training and learning in Web-based and ubiquitous environments brings a new sense of adaptation. With the development of more... Sample PDF
Using Emotional Intelligence in Personalized Adaptation
Chapter 17
Yang Wang
This chapter presents a first-of-its-kind survey that systematically analyzes existing privacy-enhanced personalization (PEP) solutions and their... Sample PDF
Technical Solutions for Privacy- Enhanced Personalization
About the Contributors