Master data

Title: Knowledgeable Explanations for Recommender Systems
Subtitle:
Abstract: Recommender Systems (RS) serve online customers in identifying those items from a variety of choices that best match their needs and preferences. In this context ecplanations summarize the reasons why a specific item is proposed and strongly increase the users' trust in the system's results. In this paper we propose a framework for generating knowledgeable explanations that exploits domain knowledge to transparently argue why a recommended item matches the users' preferences. Furthermore, results of an online experiment on a real-world platform show, that users' perception of the usability of a recommender system is positively influenced by knowledgeable explanations and that consequently users' experience in interacting with the system, their intention to use it repeatedly as well as their commitment to recommend it to others are increased.
Keywords:
Publication type: Article in compilation (Authorship)
Publication date: 2010 (Print)
Published by: Proceedings of the Web Intelligence and Intelligent Agent Technology
Proceedings of the Web Intelligence and Intelligent Agent Technology
to publication
 ( IEEE; International Conference on Web Intelligence and Intelligent Agent Technology )
Title of the series: -
Volume number: -
First publication: Yes
Page: pp. 657 - 660

Versionen

Keine Version vorhanden
Publication date: 2010
ISBN:
  • 9780769541914
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
Fakultät für Wirtschafts- und Rechtswissenschaften
 
Institut für Innovationsmanagement und Unternehmensgründung
Universitätsstraße 65-67
9020 Klagenfurt
Austria
  -994050 , -994081
   innovation@aau.at
http://www.aau.at/iug
To organisation
Universitätsstraße 65-67
AT - 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.)
working groups No working group selected

Cooperations

No partner organisations selected

Articles of the publication

No related publications