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In Search for Best Meta-Actions to Boost Businesses Revenue

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Flexible Query Answering Systems 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 400))

Abstract

The ultimate goal of our research is to build recommender system driven by action rules and meta-actions for providing proper suggestions to improve revenue of a group of clients (companies) involved with similar businesses. Collected data present answers from 25,000 customers concerning their personal information, general information about the service and customers’ feedback to the service. This paper proposes a strategy to classify and organize meta-actions in such a way that they can be applied most efficiently to achieve desired goal. Meta-actions are the triggers that need to be executed for activating action rules. In previous work, the method of mining meta-actions from customers’ reviews in text format has been proposed and implemented. Performed experiments have proven its high effectiveness. However, it turns out that the discovered action rules need more than one meta-action to be triggered. The way and the order of executing triggers causes new problems due to the commonness, differential benefit and applicability among sets of meta-actions. Since the applicability of meta-actions should be judged by professionals in the field, our concentration is put on designing a strategy to hierarchically sort and arrange so called meta-nodes (used to represent action rules and their triggers in a tree structure) as well as to compute the effect of each meta-node. Furthermore, users will have more concrete options to consider by following the path in trees built from these meta-nodes.

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References

  1. Hajja, A., Ras, Z.W., Wieczorkowska, A.: Hierarchical object-driven action rules. Journal of Intelligent Information Systems 42(2), 207–232 (2014). Springer

    Article  Google Scholar 

  2. He, Z., Xu, X., Deng, S., Ma, R.: Mining action rules from scratch. Expert Systems with Applications 29(3), 691–699 (2005). Elsevier

    Article  Google Scholar 

  3. Kuang, J., Daniel, A., Johnston, J., Raś, Z.W.: Hierarchically structured recommender system for improving nps of a company. In: Cornelis, C., Kryszkiewicz, M., Ślȩzak, D., Ruiz, E.M., Bello, R., Shang, L. (eds.) RSCTC 2014. LNCS, vol. 8536, pp. 347–357. Springer, Heidelberg (2014)

    Google Scholar 

  4. Kuang, J., Raś, Z.W., Daniel, A.: Hierarchical agglomerative method for improving nps. In: Kryszkiewicz, M., Bandyopadhyay, S., Rybinski, H., Pal, S.K. (eds.) PReMI 2015. LNCS, vol. 9124, pp. 54–64. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  5. Paul, R., Hoque, A.S.: Mining irregular association rules based on action and non-action type data. In: Proceedings of the Fifth International Conference on Digital Information Management (ICDIM), pp. 63–68 (2010)

    Google Scholar 

  6. Raś, Z.W., Wieczorkowska, A.A.: Action-rules: how to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  7. Ras, Z., Dardzinska, A.: From data to classification rules and actions. In: Proceedings of the Joint Rough Sets Symposium (JRS07). LNAI, Vol. 4482, pp. 322–329. Springer (2011)

    Google Scholar 

  8. Rauch, J., Šimůnek, M.: Action rules and the guha method: preliminary considerations and results. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 76–87. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Reichheld, F.F.: The one number you need to grow. Harvard Business Review, 1–8, December 2003

    Google Scholar 

  10. Touati, H., Kuang, J., Hajja, A., Ras, Z.: Personalized Action Rules for Side Effects Object Grouping. International Journal of Intelligence Science 3(1A), 24–33 (2013)

    Article  Google Scholar 

  11. Tzacheva, A., Ras, Z.W.: Association action rules and action paths triggered by meta-actions. In: Proceedings of 2010 IEEE Conference on Granular Computing, Silicon Valley, CA, pp. 772–776. IEEE Computer Society (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Deptartment of Computer Science, University of North Carolina, Charlotte, NC, 28223, USA

    Jieyan Kuang & Zbigniew W. Raś

  2. Institute of Computer Science, Warsaw University of Technology, 00-665, Warsaw, Poland

    Zbigniew W. Raś

Authors
  1. Jieyan Kuang
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  2. Zbigniew W. Raś
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Corresponding author

Correspondence to Zbigniew W. Raś .

Editor information

Editors and Affiliations

  1. Dept. of Comm., Business & Info. Tech., Roskilde University, Roskilde, Denmark

    Troels Andreasen

  2. Dept. Computer Science, Roskilde University, Roskilde, Denmark

    Henning Christiansen

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electronic Systems, Aalborg University, Esbjerg, Denmark

    Henrik Larsen

  5. Computer Science Department, University Milano-Biccoca, Milano, Italy

    Gabriella Pasi

  6. IRISA, University of Rennes I, Rennes, France

    Olivier Pivert

  7. Dept. of Tele. & Info. Processing, Ghent University, Gent, Belgium

    Guy De Tré

  8. Dept. of Comp. & Art. Int. Sci., University of Granada, Granada, Spain

    Maria Amparo Vila

  9. Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Adnan Yazici

  10. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

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Kuang, J., Raś, Z.W. (2016). In Search for Best Meta-Actions to Boost Businesses Revenue. In: Andreasen, T., et al. Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-319-26154-6_33

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  • DOI: https://doi.org/10.1007/978-3-319-26154-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26153-9

  • Online ISBN: 978-3-319-26154-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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