Abstract
A recommender system (RS) supports online users in e-commerce by proposing products that are assumed to be both useful and interesting. Knowledge-based recommendation systems form one branch of these online sales support systems that is particularly relevant for high-involvement product domains like consumer electronics, financial services or tourism. A constraint-based RS is a specific variant of a knowledge-based RS that builds on a CSP formalism for problem representation and solving. This article formalizes the different variants of a constraint-based recommendation problem based on consistency and the empirical part compares the performance of different constraint-based recommendation mechanisms in offline experiments on historical data.
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Zanker, M., Jessenitschnig, M. & Schmid, W. Preference reasoning with soft constraints in constraint-based recommender systems. Constraints 15, 574–595 (2010). https://doi.org/10.1007/s10601-010-9098-8
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DOI: https://doi.org/10.1007/s10601-010-9098-8