Authors:
Nikos Karacapilidis
1
and
Thomas Leckner
2
Affiliations:
1
IMIS Lab, MEAD, University of Patras, Greece
;
2
Institut für Informatik, Technische Universität München, Germany
Keyword(s):
Product Configuration, Recommender Systems, Personalization, Similarity Measures, Product Modelling
Related
Ontology
Subjects/Areas/Topics:
B2B, B2C and C2C
;
B2C/B2B Considerations
;
Business and Social Applications
;
Communication and Software Technologies and Architectures
;
e-Business
;
Enterprise Information Systems
;
e-Procurement and Web-Based Supply Chain Management
;
Market-Spaces: Market Portals, Hubs, Auctions
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Web Information Systems and Technologies
Abstract:
Adopting a mass customization strategy, enterprises often enable customers to specify their individual product wishes by using web based configurator tools. With such tools, customers can interactively and virtually create their own instance of a product. However, customers are not usually supported in a comprehensive way during the configuration process, thus facing problems such as complexity, uncertainty, and lack of knowledge. To address the above issue, this paper presents a framework that aids customers in selecting and specifying individualized products by exploiting recommendations. Having first focused on the characteristics of configurator tools and the principles of model-based configuration, we then introduce the concept of masks for product models. The main contribution of this paper is the proposal of an integrated approach for supporting model-based product configurator tools by similarity-based recommendations. Our approach in providing recommendations has been based
on the widely accepted theory of Fuzzy Sets and its associated concept of similarity measures, while recommendations provided are based on the processes of stereotype definitions and dynamic customer clustering.
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