Personalized User Preference Elicitation for e-Services

Authors:
Dietmar Jannach;Gerold Kreutler
Affiliations:
University of Klagenfurt;University of Klagenfurt
Venue:
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Year:
2005

Citing 0
Cited 7

Case-studies on exploiting explicit customer requirements in recommender systems

User Modeling and User-Adapted Interaction
Photo-based user profiling for tourism recommender systems

EC-Web'07 Proceedings of the 8th international conference on E-commerce and web technologies
Preference reasoning with soft constraints in constraint-based recommender systems

Constraints
Rapid development of knowledge-based conversational recommender applications with advisor suite

Journal of Web Engineering
Persuasive online-selling in quality and taste domains

EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Accuracy improvements for multi-criteria recommender systems

Proceedings of the 13th ACM Conference on Electronic Commerce
Enabling effective design of multimodal interfaces for speech-to-speech translation system: An empirical study of longitudinal user behaviors over time and user strategies for coping with errors

Computer Speech and Language

Quantified Score

Hi-index	0.00

Visualization

Abstract

The quality of the results produced by personalized e-service applications like product recommenders, buying advisory applications, or product configurators is strongly determinedby the accuracy of the system's estimate of the individual customer's real needs and preferences. In particular in domains where customers cannot be classified automatically, e.g., based on past buying behavior, these needs have to be interactively elicited by questioning the user. In many existing systems only a "one-style-fits-all" approach based on static fill-out forms is chosen. However, this does not take the user's background or capabilities into account, which consequently leads to a poor quality of the acquired user model. In this paper, we show how extensive personalization of the user preference elicitationprocess itself can significantly improve the accuracy of interactively acquired user models. A comprehensive view on adaptation and personalization opportunities in the elicitation process is developed and corresponding examples for the domain of interactive buying advisory are given. The presented personalization and adaptation techniques are implemented in a domain-independent software framework for building interactive advisory applications. We describe specific architectural requirements for such a system and discuss results from various real-world applications.