Integrating Semantic Knowledge with Web Usage Mining for Personalization

Integrating Semantic Knowledge with Web Usage Mining for Personalization

Honghua Dai (DePaul University, USA)
DOI: 10.4018/978-1-60566-032-5.ch010
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Abstract

Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional approaches such as collaborative filtering. Despite their success, such systems, as in more traditional ones, do not take into account the semantic knowledge about the underlying domain. Without such semantic knowledge, personalization systems cannot recommend different types of complex objects based on their underlying properties and attributes. Nor can these systems possess the ability to automatically explain or reason about the user models or user recommendations. The integration of semantic knowledge is, in fact, the primary challenge for the next generation of personalization systems. In this chapter we provide an overview of approaches for incorporating semantic knowledge into Web usage mining and personalization processes. In particular, we discuss the issues and requirements for successful integration of semantic knowledge from different sources, such as the content and the structure of Web sites for personalization. Finally, we present a general framework for fully integrating domain ontologies with Web usage mining and personalization processes at different stages, including the preprocessing and pattern discovery phases, as well as in the final stage where the discovered patterns are used for personalization.
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Introduction

With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, personalization has emerged as a critical application that is essential to the success of a Website. It is now common for Web users to encounter sites that provide dynamic recommendations for products and services, targeted banner advertising, and individualized link selections. Indeed, nowhere is this phenomenon more apparent as in the business-to-consumer e-commerce arena. The reason is that, in today’s highly competitive e-commerce environment, the success of a site often depends on the site’s ability to retain visitors and turn casual browsers into potential customers. Automatic personalization and recommender system technologies have become critical tools, precisely because they help engage visitors at a deeper and more intimate level by tailoring the site’s interaction with a visitor to her needs and interests.

Web personalization can be defined as any action that tailors the Web experience to a particular user, or a set of users (Mobasher, Cooley & Srivastava, 2000a). The experience can be something as casual as browsing a Website or as (economically) significant as trading stocks or purchasing a car. Principal elements of Web personalization include modeling of Web objects (pages, etc.) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. The actions can range from simply making the presentation more pleasing to anticipating the needs of a user and providing customized information.

Traditional approaches to personalization have included both content-based and user-based techniques. Content-based techniques use personal profiles of users and recommend other items or pages based on their content similarity to the items or pages that are in the user’s profile. The underlying mechanism in these systems is usually the comparison of sets of keywords representing pages or item descriptions. Examples of such systems include Letizia (Lieberman, 1995) and WebWatcher (Joachims, Freitag & Mitchell, 1997). While these systems perform well from the perspective of the end user who is searching the Web for information, they are less useful in e-commerce applications, partly due to the lack of server-side control by site owners, and partly because techniques based on content similarity alone may miss other types of semantic relationships among objects (for example, the associations among products or services that are semantically different, but are often used together).

User-based techniques for personalization, on the other hand, primarily focus on the similarities among users rather than item-based similarities. The most widely used technology user-based personalization is collaborative filtering (CF) (Herlocker, Konstan, Borchers & Riedl, 1999). Given a target user’s record of activity or preferences, CF-based techniques compare that record with the historical records of other users in order to find the users with similar interests. This is the so-called neighborhood of the current user. The mapping of a visitor record to its neighborhood could be based on similarity in ratings of items, access to similar content or pages, or purchase of similar items. The identified neighborhood is then used to recommend items not already accessed or purchased by the active user. The advantage of this approach over purely content-based approaches that rely on content similarity in item-to-item comparisons is that it can capture “pragmatic” relationships among items based on their intended use or based on similar tastes of the users.

The CF-based techniques, however, suffer from some well-known limitations (Sarwar, Karypis, Konstan & Riedl, 2000). For the most part these limitations are related to the scalability and efficiency of the underlying algorithms, which requires real-time computation in both the neighborhood formation and the recommendation phases. The effectiveness and scalability of collaborative filtering can be dramatically enhanced by the application of Web usage mining techniques.

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Table of Contents
Foreword
Barry Smyth
Preface
Constantinos Mourlas, Panagiotis Germanakos
Acknowledgment
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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About the Contributors