Abstract
This paper introduces an agent-based recommender system to support customized recommendations for product and service family design in electronic market environments. In this research, a preference learning mechanism is used to recommend appropriate products or services to customers and determine a preference value for each market segment in the product or service family. We demonstrate the implementation of the proposed recommender system using a multi-agent framework. Through experiments, we illustrate that the proposed recommender system can be used for customized recommendation and market segment design in various electronic market environments.
| Original language | English (US) |
|---|---|
| Title of host publication | IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Conference Proceedings |
| Pages | 824-829 |
| Number of pages | 6 |
| State | Published - 2007 |
| Event | IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Nashville, TN, United States Duration: May 19 2007 → May 23 2007 |
Other
| Other | IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World |
|---|---|
| Country/Territory | United States |
| City | Nashville, TN |
| Period | 5/19/07 → 5/23/07 |
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering