Abstract
Constraint-based recommender systems support users in the identification of interesting items from large and potentially complex assortments. Within the scope of such a preference construction process, users are repeatedly defining and revising their requirements. As a consequence situations occur where none of the items completely fulfills the set of requirements and the question has to be answered which is the minimal set of requirements that has to be changed in order to be able to find a recommendation. The identification of such minimal sets relies heavily on the identification of minimal conflict sets. Existing conflict detection algorithms are not exploiting the basic structural properties of constraint-based recommendation problems. In this paper we introduce the FastXplain conflict detection algorithm which shows a significantly better performance compared to existing conflict detection algorithms. In order to demonstrate the applicability of our algorithm we report the results of a corresponding performance evaluation.
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Schubert, M., Felfernig, A., Mandl, M. (2010). FastXplain: Conflict Detection for Constraint-Based Recommendation Problems. In: GarcÃa-Pedrajas, N., Herrera, F., Fyfe, C., BenÃtez, 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_62
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DOI: https://doi.org/10.1007/978-3-642-13022-9_62
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