Morgan Kaufmann Knowledge-Based Configuration, From Research to Business Cases (2014) by Claire Bagley & Juha Tiihonen

Author:Claire Bagley & Juha Tiihonen
Format: epub
Published: 0101-01-01T00:00:00+00:00

13.3 Integrating Recommendation Technologies to Configurators

A side effect of the high diversity of products that can be specified with a configurator is that the complexity of the offering may outstrip a user’s capability to explore the solution space and to make a buying decision. Previous research shows that consumers are often overwhelmed by the large amount of options (Malhotra, 1982). Since humans have limited processing capacity, confronting consumers with too much information can lead to an information overload and, therefore, can result in decreased quality of decision performance (Jacoby et al., 1974). Another challenge is that users often have no clear knowledge of what solution might fulfill their needs (Bettman et al., 1998). The theory of preference construction (Bettman et al.) explains that users typically do not know exactly which products or components they like (see also Mandl et al., 20145). As a consequence, users construct and adapt their preferences within the scope of a configuration process (Bettman et al., 1998).

Recommendation technologies can support users in the specification of their requirements and thus can help to achieve a higher user satisfaction (Cöster et al., 2002; Felfernig et al., 2010; Geneste and Ruet, 2001). The integration of recommendation technologies with knowledge-based configuration is still in a very early stage. There exist some contributions that take into account the application of personalization technologies in the configuration context. Geneste and Ruet (2001) introduce an approach to the integration of case-based reasoning methods (Kolodner, 1993; McSherry, 2003) with constraint solving (Tsang, 1993; Mackworth, 1977). Cöster et al. (2002), Tiihonen and Felfernig (2010), and Felfernig et al. (2010) focus on the recommendation of features, feature values, and strategies to integrate these recommendations in the user interface.

Next, we will discuss selected approaches to support users interacting with a configuration environment by the application of recommendation technologies.

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