=Paper=
{{Paper
|id=Vol-1388/demo_paper12
|storemode=property
|title=The PersonaWeb System: Personalizing E-Commerce Environments based on Human Factors
|pdfUrl=https://ceur-ws.org/Vol-1388/demo_paper12.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/GermanakosBCS15
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==The PersonaWeb System: Personalizing E-Commerce Environments based on Human Factors==
https://ceur-ws.org/Vol-1388/demo_paper12.pdf
The PersonaWeb System: Personalizing E-Commerce
Environments based on Human Factors
Panagiotis Germanakos1,2, Marios Belk1, Argyris Constantinides3, George Samaras1
1
Department of Computer Science, University of Cyprus, CY-1678 Nicosia, Cyprus
{belk,cssamara}@cs.ucy.ac.cy
2
Suite Engineering UX, Products and Innovation
SAP SE, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany
panagiotis.germanakos@sap.com
3
Department of Computer Science, University College London, London, UK
argyris.constantinides.14@ucl.ac.uk
Abstract. This demonstration paper presents the PersonaWeb system, an adap-
tive interactive system that personalizes the visual and interaction design as-
pects of E-Commerce product views based on individual differences in cogni-
tive processing. The PersonaWeb system consists of three main components: i)
the user modeling component in which explicit and implicit user data collection
methods are performed for eliciting the users’ cognitive processing factors; ii)
the content management component for creating and managing structured Web
content; and iii) the adaptive user interface that is responsible for performing
rule-based mechanisms for deciding and communicating a personalized visual
and interaction design according to the users’ cognitive characteristics.
Keywords: Human Cognitive Factors, E-Commerce, Personalization System.
1 Introduction
The growth in size and usage of applications and services in the World Wide Web has
established Internet as the ground for commercial business. With the advent of global-
ized applications and heterogeneous interaction device types, there is an increased
need to incorporate personalization techniques in E-Commerce systems in order to
more conclusively meet the requirements and needs of individuals and their overall
context of use [1, 2, 3]. Practitioners and researchers have already proposed a number
of different approaches for personalizing the visual and interaction design of E-
Commerce systems (e.g., recommender systems, responsive design, etc.) [1, 2, 3].
Nevertheless, existing personalization approaches in E-Commerce have primarily
focused on user models describing information about the users’ characteristics related
to the tasks, goals and particular domains of E-Commerce systems, and not the intrin-
sic users’ perceptual characteristics that define them as individuals (e.g., cognitive
processing abilities). Furthermore, a number of theories on individual differences
exist suggesting that users have different cognitive processing styles and abilities [4,
5], and various studies have shown that these differences affect task performance and
user preference in various application domains of interactive systems [6, 7].
Bearing in mind that users’ buying behavior and interactions in E-Commerce envi-
ronments are affected by cognitive processing factors (i.e., users are required to pro-
cess products’ information, compare features and take decisions), this research work
attempts to incorporate human cognitive differences in a user model and accordingly
adapt and personalize content and functionality of E-Commerce systems aiming to
improve task usability and provide a positive user experience. In this context, the
purpose of this paper is to present the architectural design and main components of an
adaptive interactive system, namely PersonaWeb, which aims at improving the shop-
ping experience of users by adapting the visual and interaction design of E-Commerce
Web environments based on the users’ individual differences in cognitive processing.
2 The PersonaWeb System
PersonaWeb is a Web-based interactive system that dynamically adapts content and
functionality of E-Commerce environments based on individual differences in cogni-
tive processing. Figure 1 depicts the architectural design of PersonaWeb that consists
of three main components; the User Modeling component, the Content Management
component and the Adaptive User Interface component.
Fig. 1. The PersonaWeb System Architecture.
2.1 User Modeling
The user modeling component is responsible to generate the user models of the sys-
tem which are necessary for the adaptation behavior of the system. Two cognitive
factors are used for modeling the users’ individual differences: cognitive styles and
working memory capacity. The cognitive styles’ theory is based on the Riding Cogni-
tive Style Analysis [4] which distinguishes users based on two dimensions, the Ver-
bal/Imager dimension and the Wholist/Analyst dimension. The working memory
theory is based on Baddeley’s working memory capacity model [5]. The component
supports the management and maintainability of user data collected, enabling the
administration and extension of methods and factors of the user model. Explicit and
implicit user data collection methods are used for eliciting the user models’ character-
istics. Explicit user data collection methods include accredited psychometric tests in
which users are required to respond to a series of cognitive aptitude tasks. Depending
on the users’ responses (accuracy and speed), algorithms are applied for highlighting
their cognitive processing characteristics. Implicit user data collection methods are
based on a set of Web navigation metrics in which the users’ cognitive factors are
inferred through their interactions with specific divisions in the system [8, 9].
2.2 Content Management
The content management component provides an easy to use tool for content provid-
ers to create semantically enriched Web-pages. Providers are able to annotate particu-
lar divisions of Web-pages indicating to the system which visible aspects of the E-
Commerce environment should be adapted. A WordPress 1 plugin that extends the
functionality of the default WordPress Web content editor is implemented to support
the functionality of creating Web-pages with semantically annotated content. Using
the extended editor’s methods, the content creator is able to add, edit, or delete con-
tent that is semantically annotated. The purpose of the Web content editor is to enrich
the Web-pages with semantically annotated content (e.g., title of a product, features of
a product) with the aim to indicate which divisions of the corresponding Web-page
should be adapted and how. This adaptation is based on specific rules that are applied
on the adaptive user interface which is described in the next section.
2.3 Adaptive User Interface
The adaptive user interface component takes as input the annotated content and the
users’ cognitive characteristics, and accordingly performs specific rules for adapting
the content presentation and the interaction design of the system. The adaptation pro-
cess initiates by retrieving the Web-page content from the database, which is further
parsed for extracting and separating the semantically annotated sections from the rest
of the content, with the use of client-side functions and selectors. The adaptation
mechanism runs specific rule-based statements to decide which adaptation effect de-
sign to apply, based on the users’ cognitive characteristics. Once the decision rules are
applied, the page content is reconstructed in the appropriate design and is returned to
the Web browser to be displayed. For each adaptation effect, predefined CSS (Cas-
cading Style Sheet) classes are applied on the visual design of content, prior loading
the content in the Web browser. Finally, several adaptation effects are communicated
to the users based on their cognitive styles and working memory capacity levels.
3 Conclusions
The purpose of this demonstration paper is to present the architecture and main com-
ponents of PersonaWeb, a Web personalization system that adapts content and func-
1
Wordpress, http://www.wordpress.org
tionality of E-Commerce environments based on the users’ individual differences in
cognitive processing. An extended functional prototype of the PersonaWeb system
has been designed and developed which is publicly available online2.
Several user studies have been conducted to date with over four hundred users in-
vestigating the added value of personalizing content and functionality of E-Commerce
systems in terms of task usability and user experience. Preliminary results provide
initial evidence towards using the PersonaWeb system to model human cognitive
factors and design adaptive E-Commerce Web interfaces since the studies have shown
users’ improvement in task completion efficiency and effectiveness [1, 8, 9, 10].
Acknowledgements. The work is co-funded by the Cyprus Research Promotion Foundation
project PersonaWeb (ΤΠΕ/ΠΛΗΡΟ/0311(ΒΙΕ)/10) and EU project Miraculous-Life (#611421).
4 References
1. Belk, M., Germanakos, P., Constantinides, A., Samaras, G.: A Human Cognitive Pro-
cessing Perspective in Designing E-Commerce Checkout Processes. In: Proceedings of the
International Conference on Human-Computer Interaction (INTERACT 2015), Springer
(2015) (to appear)
2. Goy, A., Ardissono, L., Petrone, G.: Personalization in E-Commerce Applications. The
Adaptive Web, Brusilovsky, P., Kobsa, A., Nejdl, W. (Eds.), pp. 485-520, Springer (2007)
3. Karat, C., Blom, J. O., Karat, J.: Designing Personalized User Experiences in eCommerce.
LNCS, Springer (2004)
4. Riding, R., Cheema, I.: Cognitive Styles – An Overview and Integration. Journal Educa-
tional Psychology, 11(3-4), 193-215 (1991)
5. Baddeley, A.: Working Memory: Theories, Models, and Controversies. Annual Review
Psychology, 63, 1-29 (2012)
6. Steichen, B., Wu, M., Toker, D., Conati, C., Carenini, G.: Te, Te, Hi, Hi: Eye Gaze Se-
quence Analysis for Informing User-Adaptive Information Visualizations. In: Proceedings
of the International Conference on User Modeling, Adaptation, and Personalization
(UMAP 2014), pp. 183-194, Springer (2014)
7. Bull, S., McCalla, G.: Modelling Cognitive Style in a Peer Help Network. Instructional
Science, 30(6), 497-528 (2000)
8. Belk, M., Papatheocharous, E., Germanakos, P., Samaras G.: Modeling Users on the
World Wide Web based on Cognitive Factors, Navigation Behaviour and Clustering Tech-
niques. Journal of Systems and Software, 86(12), 2995-3012 (2013)
9. Papatheocharous, E., Belk, M., Germanakos, P., Samaras, G.: Towards Implicit User
Modeling based on Artificial Intelligence, Cognitive Styles and Web Interaction Data. In-
ternational Journal on Artificial Intelligence Tools, 23(2), 21 pages (2014)
10. Constantinides A., Belk M., Germanakos P., Samaras G.: The PersonaCheck System for
Personalizing M-Commerce Checkout Processes. In: Proceedings of the International
Conference on Mobile Data Management (MDM 2015), IEEE Computer Society (2015)
(to appear)
2
The PersonaWeb System, https://personaweb.cs.ucy.ac.cy