An agent-based customized recommender system for product and service family design

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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 languageEnglish (US)
Title of host publicationIIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Conference Proceedings
Pages824-829
Number of pages6
StatePublished - 2007
EventIIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Nashville, TN, United States
Duration: May 19 2007May 23 2007

Other

OtherIIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World
CountryUnited States
CityNashville, TN
Period5/19/075/23/07

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All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Moon, S. K., Simpson, T. W., & Tirupatikumara, S. R. (2007). An agent-based customized recommender system for product and service family design. In IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Conference Proceedings (pp. 824-829)