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How to raise artwork prices using action rules, personalization and artwork visual features


This work explores the development of action rules for changing the prices of works of contemporary fine art. We used LISp-Miner to generate action rules related to artwork profiles and developed attributes covering artist descriptions and visual features of the artwork. We focus heavily on developing a method for partitioning a dataset to produce an increase in the coverage of the rule sets.

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This research is supported by the National Science Foundation under grant IIP 1749105. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Correspondence to Laurel Powell.

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Powell, L., Gelich, A. & Ras, Z.W. How to raise artwork prices using action rules, personalization and artwork visual features. J Intell Inf Syst (2021).

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  • Art analytics
  • Data mining
  • Action rules