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

Abstract

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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References

  1. Aggarwal, C.C. (2018). Machine learning for text. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-73531-3.

    Book  Google Scholar 

  2. Artfinder.com. (2019). https://www.artfinder.com/.

  3. Bailey, J. (2017). Machine Learning for Art Valuation. An Interview With Ahmed Hosny. https://www.artnome.com/news/2017/12/2/machine-learning-for-art-valuation.

  4. Beautiful Soup. (2019). https://www.crummy.com/software/BeautifulSoup/.

  5. Berlin, B., & Kay, P. (1969). Basic color terms: their universality and evolution. Berkeley: University of California Press.

    Google Scholar 

  6. Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with python, 1st edn. Massachusetts: O’Reilly Media Inc.

    MATH  Google Scholar 

  7. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org/10.1109/TPAMI.1986.4767851. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4767851.

    Article  Google Scholar 

  8. Colors by name. (2020). http://colormine.org/colors-by-name.

  9. Galbraith, J., & Hodgson, D. (2018). Econometric fine art valuation by combining hedonic and repeat-sales information. Econometrics, 6(3), 32. https://doi.org/10.3390/econometrics6030032. http://www.mdpi.com/2225-1146/6/3/32.

    Article  Google Scholar 

  10. Hajja, A., Raś, Z.W., & Wieczorkowska, A.A. (2014). Hierarchical object-driven action rules. Journal of Intelligent Information Systems, 42(2), 207–232. https://doi.org/10.1007/s10844-013-0291-2.

    Article  Google Scholar 

  11. Hosny, A., Huang, J., & Wang, Y. (2014). The Green Canvas. http://ahmedhosny.github.io/theGreenCanvas/.

  12. Hutto, C., & Gilbert, E. (2015). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the 8th international conference on weblogs and social media, ICWSM 2014.

  13. Kalanat, N., & Khanjari, E. (2020). Action extraction from social networks. Journal of Intelligent Information Systems, 54(2), 317–339. https://doi.org/10.1007/s10844-019-00551-2.

    Article  Google Scholar 

  14. Kang, D., Shim, H., & Yoon, K. (2018). A method for extracting emotion using colors comprise the painting image. Multimedia Tools and Applications, 77(4), 4985–5002. https://doi.org/10.1007/s11042-017-4667-0.

    Article  Google Scholar 

  15. Labrecque, L.I., & Milne, G.R. (2012). Exciting red and competent blue: the importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), 711–727. https://doi.org/10.1007/s11747-010-0245-y.

    Article  Google Scholar 

  16. Lindsey, D.T., & Brown, A.M. (2006). Universality of color names. Proceedings of the National Academy of Sciences, 103(44), 16608–16613. https://doi.org/10.1073/pnas.0607708103.

    Article  Google Scholar 

  17. Liu, D., & Woodham, D. (2019). Using AI to Predict Rothko paintings’ auction prices. https://www.artsy.net/article/artsy-editorial-ai-predict-mark-rothko-paintings-auction-prices.

  18. Lombardi, T.E. (2005). The classification of style in fine-art painting. Ph.D. thesis. https://librarylink.uncc.edu/login?url=https://search-proquest-com.librarylink.uncc.edu/docview/305390005?accountid=14605. Copyright - Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; Last updated - 2020-02-21.

  19. Mardini, M.T., & Raś, Z.W. (2019). Extraction of actionable knowledge to reduce hospital readmissions through patients personalization. Information Sciences, 485, 1–17. https://doi.org/10.1016/j.ins.2019.02.006. https://www.sciencedirect.com/science/article/pii/S0020025519301008.

    Article  Google Scholar 

  20. Mehrabian, A. (1978). Measures of individual differences in temperament. Educational and Psychological Measurement, 38(4), 1105–1117. https://doi.org/10.1177/001316447803800431.

    Article  Google Scholar 

  21. Nekvapil, V. (2009). Using the ac4ft-miner procedure in the medical domain [online] [cit. 2020-01-03] https://theses.cz/id/0abafc/?lang=en.

  22. Ou, L., Luo, M.R., Woodcock, A., & Wright, A. (2004). A study of colour emotion and colour preference. part i: Colour emotions for single colours. Color Research & Application, 29(3), 232–240. https://doi.org/10.1002/col.20010.

    Article  Google Scholar 

  23. Pawlowski, C., Gelich, A., & Raś, Z.W. (2019). Can We Build Recommender System for Artwork Evaluation? In R. Bembenik, Skonieczny, G. Protaziuk, M. Kryszkiewicz, & H. Rybinski (Eds.) Intelligent Methods and Big Data in Industrial Applications. https://doi.org/10.1007/978-3-319-77604-0_4 (pp. 41–52). Cham: Springer International Publishing.

  24. Powell, L., Gelich, A., & Ras, Z.W. In T. Mihálydeák, F. Min, G. Wang, M. Banerjee, I. Düntsch, Z. Suraj, & D. Ciucci (Eds.) (2019). Developing artwork pricing models for online art sales using text analytics. Cham: Springer International Publishing.

  25. Powell, L., Gelich, A., & Ras, Z.W. (2020). Applying analytics to artist provided text to model prices of fine art. In Complex pattern mining: New challenges, methods and applications, studies in computational intelligence, (Vol. 880 pp. 189–211). Springer.

  26. Powell, L., Gelich, A., & Ras, Z.W. (2020). Art innovation systems for value tagging. In Encyclopedia of organizational knowledge, administration, and technologies. IGI Global. https://www.igi-global.com/book/encyclopedia-organizational-knowledge-administration-technology/242894.

  27. Powell, L., Gelich, A., & Ras, Z.W. In D. Helic, G. Leitner, M. Stettinger, & Z.W. Ras (Eds.) (2020). The construction of action rules to raise artwork prices. Austria: Springer.

  28. Ras, Z.W., & Wieczorkowska, A. In D. A. Zighed, J. Komorowski, & J. Żytkow (Eds.) (2000). Action-rules: How to increase profit of a company. Berlin: Springer.

  29. Rauch, J., & Šimůnek, M. In J. Rauch, Z.W. Raś, P. Berka, & T. Elomaa (Eds.) (2009). Action rules and the guha method: Preliminary considerations and results. Berlin: Springer.

  30. Rauch, J., Šimůnek, M., Berka, P., Černý, Z., Dolejší, P., Karban, T., Kejkula, M., Lin, V., Máša, P., Spáčil, P., Strossa, P., Šebek, M., Šlesinger, J., Šubrt, M., Turunen, E., & Vanžura, F. (2019). https://lispminer.vse.cz/index.html.

  31. Rawlins, C., & Johnson, P. (2007). Selling on eBay: Persuasive communication advice based on analysis of auction item descriptions. Journal of Strategic E-commerce, 5(1&2), 75–81.

    Google Scholar 

  32. Roberson, D., & Hanley, J. (2007). Color vision: Color categories vary with language after all. Current Biology, 17(15), R605–R607. https://doi.org/10.1016/j.cub.2007.05.057. http://www.sciencedirect.com/science/article/pii/S0960982207014819.

    Article  Google Scholar 

  33. Russell, J.A., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of Research in Personality, 11(3), 273–294.

    Article  Google Scholar 

  34. Saleh, B., & Elgammal, A. (2015). Large-scale classification of fine-art paintings: Learning the right metric on the right feature. arXiv:1505.00855.

  35. Selenium. (2019). https://www.seleniumhq.org/.

  36. Sharma, G., Wu, W., & Dalal, E.N. (2004). The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application, 30(1), 21–30. https://doi.org/10.1002/col.20070.

    Article  Google Scholar 

  37. Tarnowska, K., Daniel, L., & Ras, Z.W. (2020). Recommender System for Improving Customer Loyalty, 1st ed. 2020 edn. No. 55 in Studies in Big Data. Springer International Publishing : Imprint. Cham: Springer. https://doi.org/10.1007/978-3-030-13438-9.

    Google Scholar 

  38. Tarnowska, K.A., Ras, Z.W., & Jastreboff, P.J. (2017). Mining for actionable knowledge in tinnitus datasets. In G. Wang, A. Skowron, Y. Yao, D. Ślezak, & L. Polkowski (Eds.) Thriving Rough Sets: 10th Anniversary - Honoring Professor Zdzisław Pawlak’s Life and Legacy & 35 Years of Rough Sets. https://doi.org/10.1007/978-3-319-54966-8_18 (pp. 367–395). Cham: Springer International Publishing.

  39. Taylor, G. (2014). python-colormath.

  40. Team, O. (2017). OpenCV.

  41. The Hiscox Online Art Trade Report. (2018). Colors by Name. Tech. rep., ArtTactic 2018. https://arttactic.com/product/hiscox-online-art-trade-report-2018/.

  42. Tsay, L.S., & Raś, Z.W. (2005). Action rules discovery: system DEAR2, method and experiments. Journal of Experimental & Theoretical Artificial Intelligence, 17(1-2), 119–128. https://doi.org/10.1080/09528130512331315855. Publisher: Taylor & Francis.

    Article  Google Scholar 

  43. Tzacheva, A.A., Bagavathi, A., & Ayila, L. (2017). Discovery of action rules at lowest cost in spark. In 2017 IEEE international conference on data mining workshops (ICDMW). https://doi.org/10.1109/ICDMW.2017.173. http://ieeexplore.ieee.org/document/8215648/ (pp. 87–94). New Orleans: IEEE.

  44. Tzacheva, A.A., & Raś, Z.W. (2005). Action rules mining. International Journal of Intelligent Systems, 20(7), 719–736. https://doi.org/10.1002/int.20092.

    Article  Google Scholar 

  45. Valdez, P., & Mehrabian, A. (1994). Effects of color on emotions. Journal of Experimental Psychology: General, 123(4), 394–409. https://doi.org/10.1037/0096-3445.123.4.394.

    Article  Google Scholar 

  46. Velthuis, O. (2005). Talking prices: symbolic meanings of prices on the market for contemporary art. Princeton: Princeton University Press. http://www.jstor.org/stable/j.ctt4cgd14.

    Google Scholar 

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Acknowledgements

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). https://doi.org/10.1007/s10844-021-00660-x

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Keywords

  • Art analytics
  • Data mining
  • Action rules