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Preference reasoning with soft constraints in constraint-based recommender systems

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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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Authors and Affiliations

  1. Universität Klagenfurt, Universitätsstrasse 65-67, 9020, Klagenfurt, Austria

    Markus Zanker, Markus Jessenitschnig & Wolfgang Schmid

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  1. Markus Zanker
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  2. Markus Jessenitschnig
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  3. Wolfgang Schmid
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Correspondence to Markus Zanker.

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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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