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Lean improvement of the stage shows in theme park based on consumer preferences correlation deep mining

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Abstract

Online comments provide a new and convenient way to understand consumer preference, but these comments for stage shows in theme park are usually incomplete, which can seriously affect the accuracy of existing mining models. In order to overcome the dilemma of missing information, we propose the consumer preferences correlation deep mining model, which precisely mines user preferences from two aspects: comment semantic deep mining and attribute emotion correlation mining. Furthermore, the Kano-IPA model is proposed to comprehensively excavate the user satisfaction and the importance of product attributes to give a lean improvement strategy for stage shows. Specifically, firstly, correlation deep mining model is constructed to deeply mine the missing attribute emotional polarity based on the emotional correlation sequence, emotional vector and Senti2vec + Gated Recurrent Unit model. Secondly, correlation width mining model is developed to excavate the user preferences for the stage shows attribute. In the correlation width mining model, the partial regression equation is used to describe the influence of the user emotional polarity on the user satisfaction level. Based on the emotion correlated attribute sequences, the correlation Kano mapping rules are proposed, and then the priority of user preferences for product attributes is given. Thirdly, the Kano-IPA model is designed for the lean improvement of products to achieve higher benefits at a lower cost. Finally, the experimental results on Shanghai Disneyland confirm the effectiveness and application value of the proposed model. Consequently, this study provides an accurate decision support model driven by big data for product improvement.

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References

  1. Bai S, Ren J, Ba H, Zhou H, Yang R (2017) Dynamic evaluation of consumer satisfaction based on the grey prediction model. In: 2017 4th international conference on information science and control engineering (ICISCE). IEEE computer society, pp 270–277

  2. Batavio AB, Tripiawan W, Amani H (2017) Consumer preference in using the services of bukalapak website with conjoint method. IOP Conference Series. Mater Sci Eng 277:012003

    Google Scholar 

  3. Calegari LP, Barbosa J, Marodin GA, Fettermann DC (2018) A conjoint analysis to consumer choice in Brazil: defining device attributes for recognizing customized foods characteristics. Food Res Int 109:1–13

    Article  Google Scholar 

  4. Chong AYL, Eugene C’n, Liu MJ, Li B (2015) Predicting consumer product demands via big data: the roles of online promotional marketing and online reviews. Int J Prod Res 55(17):1–15

    Google Scholar 

  5. De Pelsmaeker S, Schouteten JJ, Lagast S, Dewettinck K, Gellynck X (2017) Is taste the key driver for consumer preference? A conjoint analysis study. Food Qual Prefer 62:323–331

    Article  Google Scholar 

  6. Garcia-Laencina PJ, Sancho-Gbmez JL, Figuerias-Vidal AR, Verleysen M (2009) K nearest neighbours with mutual information for simultaneous classification and missing data imputation. Neurocomputing 72:1483–1493

    Article  Google Scholar 

  7. Hron K, Templ M, Filzmoser P (2011) Imputation of missing values for compositional data using classical and robust methods. Comput Stat Data An 54(12):3095–3107

    Article  MathSciNet  Google Scholar 

  8. Hsieh PL, Yeh TM, Chen JE (2015) Integrating fuzzy SERVQUAL into refined Kano model to determine the critical service quality attributes of chain restaurants. Rev Integr Bu Econ Res 4(4):142–157

    Google Scholar 

  9. Juan L, He F (2011) Application of Grey correlation analysis to the evaluation of customer satisfaction on ceramic products. In: International Conference on Computational & Information Sciences, IEEE, pp 442–444

  10. Kalchschmidt M, Zotteri G, Verganti R (2003) Inventory management in a multi-echelon spare parts supply chain. Int J Prod Econ 81–82(1):397–413

    Article  Google Scholar 

  11. Kano N, Seraku N, Takahashi F, Tsuji S (1984) Attractive quality and must-be quality. J JPN Soc Qual Contr 14(2):147–156

    Google Scholar 

  12. Kerzner H (2017) Industry specific: Disney theme parks. Project management case studies. John Wiley & Sons, Inc.

  13. Kumar KLS, Desai J, Majumdar J (2016) Opinion mining and sentiment analysis on online customer review. In: IEEE International Conference on Computational Intelligence & Computing Research, pp 1–4

    Google Scholar 

  14. Kuroda M, Sakakihara M (2006) Accelerating the convergence of the EM algorithm using the vector ε algorithm. Elsevier Science Publishers B. V, Accelerating the convergence of the EM algorithm using the vector algorithm.

  15. Liao SH, Chang HK (2016) A rough set-based association rule approach for a recommendation system for online consumers. Inf Process Manag 52(6):1142–1160

    Article  Google Scholar 

  16. Little RJA (1992) Regression with missing X’ s: a review. J Am Stat Assoc 87(420):1227–1237

    Google Scholar 

  17. Mikulic J, Prebezac D (2012) Accounting for dynamics in attribute-importance and for competitor performance to enhance reliability of BPNN-based importance–performance analysis. Expert Syst Appl 39(5):5144–5153

    Article  Google Scholar 

  18. Miranda S, Tavares P, Queiro R (2017) Perceived service quality and customer satisfaction: a fuzzy set QCA approach in the railway sector. J Bus Res 89:371–377

    Article  Google Scholar 

  19. Pradhan N, Deolalikar V, Singh D (2018) Islands of interest: mining concentrations of user search intent over e-commerce product categories. In: proceedings - 2018 IEEE international conference on big data, pp 3717–3722

    Chapter  Google Scholar 

  20. Wang Y, Wan W, Wang RS, Feng E (2009) Model, properties and imputation method of missing SNP genotype data utilizing mutual information. J Comput Appl Math 229(1):168–174

    Article  MathSciNet  Google Scholar 

  21. Zeithaml VA, Berry LL, Parasuraman A (1996) The behavioral consequences of service quality. J Mark 60(2):31–46

    Article  Google Scholar 

  22. Zhang Y, Li X, Su Q, Hu X (2017) Exploring a theme park’s tourism carrying capacity: a demand-side analysis. Tour Manag 59:564–578

    Article  Google Scholar 

  23. Kuang C J (2018) An integrated fuzzy MICMAC with a revised IPA approach to explore service quality improvement. Total Qual Manag bus excellence 1-19

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Acknowledgements

This work was supported by the Chinese National Natural Science Foundation (No. 71871135).

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Authors and Affiliations

  1. School of management, Shanghai University, Shanghai, 200444, People’s Republic of China

    Shugang Li, Hanyu Lu, Jiali Kong & Ru Wang

  2. Industrial and commercial bank of China Shanghai branch, Shanghai, 200131, People’s Republic of China

    Jiali Kong

  3. Department of automation, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Zhaoxu Yu

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  1. Shugang Li
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  2. Hanyu Lu
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  3. Jiali Kong
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  4. Zhaoxu Yu
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  5. Ru Wang
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Correspondence to Jiali Kong.

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Li, S., Lu, H., Kong, J. et al. Lean improvement of the stage shows in theme park based on consumer preferences correlation deep mining. Multimed Tools Appl 79, 24487–24506 (2020). https://doi.org/10.1007/s11042-020-09112-0

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  • DOI: https://doi.org/10.1007/s11042-020-09112-0

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