Automated debugging of recommender user interface descriptions

Abstract Customers interacting with online selling platforms require the assistance of sales support systems in the product and service selection process. Knowledge-based recommenders are specific sales support systems which involve online customers in dialogs with the goal to support preference forming processes. These systems have been successfully deployed in commercial environments supporting the recommendation of, e.g., financial services, e-tourism services, or consumer goods. However, the development of user interface descriptions and knowledge bases underlying knowledge-based recommenders is often an error-prone and frustrating business. In this paper we focus on the first aspect and present an approach which supports knowledge engineers in the identification of faults in user interface descriptions. These descriptions are the input for a model-based diagnosis algorithm which automatically identifies faulty elements and indicates those elements to the knowledge engineer. In addition, we present results of an empirical study which demonstrates the applicability of our approach.

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