An introduction to personalization and mass customization

A key enabling technology of mass customization is knowledge-based configuration (Felfernig et al. 2014; Shafiee et al. 2016; Stumptner 1997). Early configuration systems² were implemented on the basis of rule-based technologies (McDermott 1982). Model-based approaches (Mittal and Frayman 1989) predominate in existing systems, especially when the underlying knowledge bases become large and complex. Model-based approaches allow a clear separation between domain knowledge and corresponding problem solving knowledge. Hence they avoid an intermingling of both knowledge types, which has significantly increased related development and maintenance efforts (Soloway et al. 1987). Configuration technologies are applied in various domains such as telecommunication switches (Fleischanderl et al. 1998), computer hardware (McDermott 1982; Henson 2000), industrial machinery such as compressors and tractors (Tiihonen et al. 2013), Internet of Things (IoT) (Felfernig et al. 2016), and financial services (Leist and Winter 1994). For an overview of applications and a discussion about the history of configuration technologies, we refer to Felfernig et al. (2014). A sketch of the major components of a configuration environment is provided in Fig. 1.

Many existing commercial configuration environments include constraint technologies (Tsang 1993) to support the solving of configuration tasks (Desisto 2004; Felfernig et al. 2014). In order to make configuration technologies applicable in interactive settings such as a web-based configurator, related reasoning engines must be able to provide response times that are below one second (Card et al. 1991). Such settings require the provision of efficient reasoning engines that often exploit compiled knowledge representations such as binary decision diagrams (BDDs) (Andersen et al. 2010) to more efficiently determine whether a solution exists for the given configuration task. In situations where no solution can be identified for a given set of customer requirements, conflict detection (Junker 2004) and diagnosis approaches (Bakker et al. 1993; Felfernig et al. 2004; Felfernig et al. 2012) are applied to assist users in finding a way out from the no solution could be found dilemma.

The overall idea of personalization is to tailor contents to known wishes and needs of a specific user – related information is stored in a user model that is used to extrapolate which items such as products, services or units of information should be shown to a user. There are various AI technologies that support the identification of items that should be shown to a user; an example thereof are recommendation technologies (Jannach et al. 2010). For example, collaborative filtering recommends to the current user items that have previously been positively evaluated by users with preferences similar to the current user. Furthermore, content-based recommendation approaches are based on the idea of analyzing the items purchased by the current user and recommend similar items in the future. Finally, knowledge-based recommenders identify relevant items on the basis of a set of filter constraints (Jannach et al. 2010). A sketch of the major components of a recommendation environment is provided in Fig. 2.

There are a number of approaches to integrate the ideas of recommender systems into knowledge based configuration environments. Starting with Coester et al. (2002), further recommendation approaches have been proposed (Tiihonen and Felfernig 2010). The most recent work on the topic can be found in Fargier et al. (2016) where recommendation algorithms are evaluated on the basis of real-world configuration knowledge bases. The application of recommendation approaches is not restricted to solution search; it has also been shown how recommendation technologies can be exploited interactively to determine personalized diagnoses in situations where no solution meets the preferences that the user specified (Felfernig et al. 2009). A diagnosis approach that combines high prediction quality with highly efficient diagnostic reasoning is introduced in Felfernig et al. (2012).

This special issue comprises articles related to the areas of personalization and mass customization. In the following, we provide an overview of these articles.

Supporting the social dimension of shopping for personalized products through online sales configurators. On the basis of an analysis of the existing state-of-the-art, Grosso et al. (2016) introduce a classification of different approaches to enhance the shopping experience of users purchasing complex products and services with a corresponding social dimension. A simple example thereof is the communication of a purchase on Facebook where the basic properties of the configuration are shared. This social dimension does not only play an important role for users (e.g., being able to inform friends about his/her new purchases) but also for companies in terms of being able to exploit user engagement to popularize their product assortments. The contribution of Grosso et al. (2016) can be interpreted as a simple form of collaborative recommendation since a group of friends is notified about items that have been purchased by the current user.

The articles of this special issue provide relevant contributions to advance the state-of-the-art in the field of personalization and mass customization. A number of research issues exist from our point of view that have to be tackled by future research.