@article{645,
title = {A Web Metrics quality evaluation framework for e- commerce systems},
author = {Antonia Stefani and Vasileios Vasileiadis},
url = {http://daissy.eap.gr/wp-content/uploads/2015/03/2.A-Web-Metrics-quality-evaluation-framework.pdf},
year = {2013},
date = {2013-01-01},
journal = {Computer Standards and Interfaces Journal},
abstract = {E-commerce B2C systems are diverse and their quality is difficult to be measured without a concrete methodology. In this paper we present a B2C-specific quality evaluation framework for web metrics that helps understand what needs to be measured and how. The framework uses three dimensions based on end-user interaction categories, metrics internal specs and quality sub-characteristics as defined of ISO9126. Beginning from the existing large corpus of general-purpose web metrics, specific metrics used for quality evaluation of ecommerce systems are chosen and categorized. Analysis results are subjected to a data mining analysis in order to provide association rules between the various dimensions of the framework. Finally, an ontology that corresponds to the framework is developed to answer to complicated questions related to metrics use and to facilitate the production of new, user-defined meta-metrics .},
keywords = {E-commerce, evaluation framework, ISO 9126, Ontology, quality attributes, taxonomy, web metrics},
pubstate = {published},
tppubtype = {article}
}
E-commerce B2C systems are diverse and their quality is difficult to be measured without a concrete methodology. In this paper we present a B2C-specific quality evaluation framework for web metrics that helps understand what needs to be measured and how. The framework uses three dimensions based on end-user interaction categories, metrics internal specs and quality sub-characteristics as defined of ISO9126. Beginning from the existing large corpus of general-purpose web metrics, specific metrics used for quality evaluation of ecommerce systems are chosen and categorized. Analysis results are subjected to a data mining analysis in order to provide association rules between the various dimensions of the framework. Finally, an ontology that corresponds to the framework is developed to answer to complicated questions related to metrics use and to facilitate the production of new, user-defined meta-metrics .
@proceedings{65,
title = {Adaptation strategies: a comparison between e-learning and e-commerce techniques},
author = {Vasileios Vasileiadis and Antonia Stefani},
year = {2012},
date = {2012-01-01},
journal = {8th Artificial Intelligence Applications and Innovations (AIAI 2012).},
volume = {IFIP AICT 382},
pages = {115–124},
address = {Halkidiki, Greece},
chapter = {Halkidiki, Greece},
abstract = {
The importance of e-learning and e-commerce applications has significantly increased in the past few years. Seeking better design and implementation principles is a research goal with, potentially, a significant impact. One of the commonalities of both applications is user-centricity. Understanding user behavior is critical especially in user-centered applications such as e-commerce and e-learning. In this work we discuss some of the fundamental similarities and differences in e-commerce and formal e-learning adaptation and discuss lessons that could be learned. We argue that current user pattern mining techniques should take into account behavioral and educational theories for distance learning in order to be efficient.
<div style="text-align: justify;">The importance of e-learning and e-commerce applications has <span style="line-height: 1.538em;">significantly increased in the past few years. Seeking better design and </span><span style="line-height: 1.538em;">implementation principles is a research goal with, potentially, a significant </span><span style="line-height: 1.538em;">impact. One of the commonalities of both applications is user-centricity. </span><span style="line-height: 1.538em;">Understanding user behavior is critical especially in user-centered applications </span><span style="line-height: 1.538em;">such as e-commerce and e-learning. In this work we discuss some of the </span><span style="line-height: 1.538em;">fundamental similarities and differences in e-commerce and formal e-learning </span><span style="line-height: 1.538em;">adaptation and discuss lessons that could be learned. We argue that current user </span><span style="line-height: 1.538em;">pattern mining techniques should take into account behavioral and educational </span><span style="line-height: 1.538em;">theories for distance learning in order to be efficient.</span></div>
