GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Guest Editors' Introduction: Recommender Systems
IEEE Intelligent Systems
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Proceedings of the 13th international conference on Intelligent user interfaces
The VITA financial services sales support environment
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Generating and evaluating evaluative arguments
Artificial Intelligence
Persuasive recommendation: serial position effects in knowledge-based recommender systems
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
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Knowledge-based recommenders support customers in preference construction processes related to complex products and services. In this context, utility constraints (scoring rules) play an important role. They determine the order in which items (products and services) are presented to customers. In many cases utility constraints are faulty, i.e., calculate rankings which are not expected and accepted by marketing and sales experts. The adaptation of these constraints is extremely time-consuming and often an error-prone process. In this paper we present an approach which effectively supports the automated adaptation of utility constraint sets based on solutions for corresponding nonlinear optimization problems. This approach significantly increases the applicability of knowledge-based recommendation by allowing the automated reproduction of item rankings specified by marketing and sales experts.