Abstract
Constraint-based recommender applications provide valuable support in item selection processes related to complex products and services. This type of recommender operates on a knowledge base that contains a deep model of the product assortment as well as constraints representing the company’s marketing and sales rules. Due to changes in the product assortment as well as in marketing and sales rules, such knowledge bases have to be adapted very quickly and frequently. In this paper we focus on a specific but very important aspect of recommender knowledge base development: we analyze the impact of different constraint representations on the cognitive effort of a knowledge engineer to successfully complete certain knowledge acquisition tasks. In this context, we report results of an initial empirical study and provide first basic recommendations regarding the design of recommender knowledge bases.
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Felfernig, A., Mandl, M., Pum, A., Schubert, M. (2010). Empirical Knowledge Engineering: Cognitive Aspects in the Development of Constraint-Based Recommenders. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_63
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DOI: https://doi.org/10.1007/978-3-642-13022-9_63
Publisher Name: Springer, Berlin, Heidelberg
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