Dimitrios Pierrakos

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This paper is a survey of recent work in the field of web usage mining for the benefitof research on the personalization of Web-based information services. The essence of personalization is the adaptability of information systems to the needs of their users. This issue is becoming increasingly important on the Web, as non-expert users are overwhelmed by the(More)
INTRODUCTION The hypergraphical architecture of the Web has been used to support claims that the Web will make Internetbased services really user-friendly. However, at its current state, the Web has not achieved its goal of providing easy access to online information. Being an almost unstructured and heterogeneous environment it creates an information(More)
The growth of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is open source software which consists of a(More)
This paper presents a knowledge discovery framework for the construction of Community Web Directories, a concept that we introduced in our recent work, applying personalization to Web directories. In this context, the Web directory is viewed as a thematic hierarchy and personalization is realized by constructing user community models on the basis of usage(More)
This paper improves a recently-presented approach to Web Personalization, named Community Web Directories, which applies personalization techniques to Web Directories. The Web directory is viewed as a concept hierarchy and personalization is realized by constructing user community models on the basis of usage data collected by the proxy servers of an(More)
This paper introduces a new approach to Web Personalization, named Web Community Directories that aims to tackle the problem of information overload on the WWW. This is realized by applying personalization techniques to the well-known concept of Web Directories. The Web directory is viewed as a concept hierarchy which is generated by a content-based(More)
This paper presents the concept of Web Community Directories, as a means of personalizing services on the Web, together with a novel methodology for the construction of these directories by document clustering and usage mining methods. The community models are extracted with the use of the Community Directory Miner, a simple cluster mining algorithm which(More)
We propose a knowledge framework for garment recommendations, which is based on two pillars. The first pillar incorporates knowledge about aspects of fashion, such as materials, garments, colours, body types, facial features, social occasion etc., as well as their interrelations, with the purpose of providing personalised recommendations. The said knowledge(More)
We present a method for modeling user navigation on a web site using grammatical inference of stochastic regular grammars. With this method we achieve better models than the previously used first order Markov chains, in terms of predictive accuracy and utility of recommendations. In order to obtain comparable results, we apply the same grammatical inference(More)