Master data

Title: A generic user modeling component for hybrid recommendation strategies
Subtitle:
Abstract: Over the last decade, recommendation systems (RS) have matured into a valuable approach for assisting online customers in navigating through large product or information spaces. The associated research has described and evaluated a variety of different techniques for proposing items of interest to customers. However, each of these techniques also suffers from several shortcomings. Therefore, depending on the application domain and the availability of background knowledge some algorithms and hybrid variants may be more applicable than others. However, most commercial recommendation systems are monolithic in the sense that they support only a limited subset of recommendation techniques.In this paper we therefore present ISeller, a proven industrial-strength recommendation framework for personalizing small to medium-scale e-commerce platforms. ISeller supports all basic recommendation techniques and, due to its modular architecture, hybrid variants as well. This paper focuses in particular on the generic user modeling component of ISeller as it is the prerequisite for supporting different recommendation techniques within the same application infrastructure. Furthermore, we present an application scenario showing the generic nature and wide applicability of the described user modeling component in the domain of map-based recommendations.
Keywords:
Publication type: Article in compilation (Authorship)
Publication date: 07.2009 (Print)
Published by: Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
to publication
 ( IEEE; B. Hofreiter )
Title of the series: -
Volume number: 11
First publication: Yes
Page: pp. 337 - 344

Versionen

Keine Version vorhanden
Publication date: 07.2009
ISBN:
  • 9780769537559
ISSN: -
Homepage: -

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Artificial Intelligence und Cybersecurity
Universitätsstr. 65-67
A-9020 Klagenfurt
Austria
  -993705
   aics-office@aau.at
https://www.aau.at/en/aics/
To organisation
Universitätsstr. 65-67
AT - A-9020  Klagenfurt

Categorisation

Subject areas
  • 1108 - Informatics
Research Cluster No research Research Cluster selected
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: n.a.)
Classification raster of the assigned organisational units:
working groups No working group selected

Cooperations

No partner organisations selected

Articles of the publication

No related publications