Data Mining Techniques for Web Personalization: Algorithms and Applications

Data Mining Techniques for Web Personalization: Algorithms and Applications

Gulden Uchyigit (University of Brighton, UK)
DOI: 10.4018/978-1-60566-908-3.ch001
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

The increase in the information overload problem poses new challenges in the area of web personalization. Traditionally, data mining techniques have been extensively employed in the area of personalization, in particular data processing, user modeling and the classification phases. More recently the popularity of the semantic web has posed new challenges in the area of web personalization necessitating the need for more richer semantic based information to be utilized in all phases of the personalization process. The use of the semantic information allows for better understanding of the information in the domain which leads to more precise definition of the user’s interests, preferences and needs, hence improving the personalization process. data mining algorithms are employed to extract richer semantic information from the data to be utilized in all phases of the personalization process. This chapter presents a stateof- the-art survey of the techniques which can be used to semantically enhance the data processing, user modeling and the classification phases of the web personalization process.
Chapter Preview
Top

Introduction

Personalization technologies have been popular in assisting users with the information overload problem. As the number of services and the volume of content continues to grow personalization technologies are more than ever in demand.

Mobasher (Mobasher et al., 2004) classifies web personalization into 3 groups. These are, manual decision rule systems, content-based recommender systems and collaborative based recommender systems. Manual decision rule systems allow the web site administrator to specify rules based on user demographics or on static profiles (collected through a registration process). Content-based recommender systems make personalized recommendations based on user profiles. Collaborative-based recommender systems make use of user ratings and make recommendations based on how other users in the group have rated similar items.

Data mining techniques have extensively been used in personalization systems, for instance text mining algorithms such as feature selection are employed in content-based recommender systems as way of representing user profiles. Other data mining algorithms such as clustering and rule learning algorithms are employed in collaborative recommender systems.

In recent years developments into extending the Web with semantic knowledge in an attempt to gain a deeper insight into the meaning of the data being created, stored and exchanged has taken the Web to a different level. This has lead to developments of semantically rich descriptions to achieve improvements in the area of personalization technologies (Pretschner and Gauch, 2004). Utilizing such semantic information provides a more precise understanding of the application domain, and provides a better means to define the user's needs, preferences and activities with regard to the system, hence improving the personalization process. Here data mining algorithms are employed to extract semantic meaning from data such as ontologies. Here, algorithms such as clustering, fuzzy sets, rule learning algorithms, Natural language processing have been employed.

This chapter will present an overview of the state-of-the art techniques in the use of data mining techniques in personalization systems, and how they have been and will continue to shape personalization systems.

Complete Chapter List

Search this Book:
Reset