Abstract
In recent days, big data based analysis in hotel industry become popular. Merchants are attracting clients using the accurate analysis of historic data and predicting the behavior of possible clients to perform proper marketing strategy. To study the principle of the game between clients and merchants, in this work, we propose a novel two-stage game theoretic approach of decision policy for clients when choosing the suitable hotel to stay among many candidates, the merchants will provide a non-cooperative game strategy to attract the attention of potential clients. Analysis of the non-cooperative game method based on big data has been given. Simulation results indicate that, by using our proposed novel method, the average price for clients to choose a satisfied hotel is reduced and the successful rate of stay is increased for merchants, which will bring the expected income to a higher level because of the sticky phenomena of users.





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References
Adolph, M. (2014). Big data, its enablers and standards. PIK—Praxis der Information sverarbeitung und Kommunikation, 37(3), 197–204.
Asay, M. (2013). Q&A. Is open source sustainable? Technology innovation. Management Review, 3(1), 46–49.
Banerjee, S., Viswanathan, V., Raman, K., & Ying, H. (2013). Assessing prime-time for geotargeting with mobile big data. Journal of Marketing Analytics, 1(3), 174–183.
Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(10), 78–83.
Bennett, M. (2013). The financial industry business ontology: Best practice for big data. Journal of Banking Regulation, 14(3/4), 255–268.
Bertot, J. C., Gorham, U., Jaeger, P. T., Sarin, L. C., & Choi, H. (2014). Big data, open government and e-government: Issues, policies and recommendations. Information Polity: The International Journal of Government & Democracy in the Information Age, 19(1), 5–16.
Bharadwaj, A., El Sawy, O. E., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482.
Bone, S. A., Fombelle, P. W., Ray, K. R., & Lemon, K. N. (2014). How customer participation in B2B peer-to-peer problem-solving communities influences the need for traditional customer service. Journal of Service Research, 18(1), 23–38.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165–1188.
Chow-White, P. A., & Green, J. G. (2013). Data mining difference in the age of big data: Communication and the social shaping of genome technologies from 1998 to 2007. International Journal of Communication, 7, 28.
Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412–421.
Dobre, C., & Xhafa, F. (2014). Parallel programming paradigms and frameworks in big data era. International Journal of Parallel Programming, 42(5), 710–738.
Fanning, K., & Grant, R. (2013). Big data: implications for financial managers. Journal of Corporate Accounting & Finance, 24(5), 23–30.
Gabel, T. J., & Tokarski, C. (2014). Big data and organization design. Journal of Organization Design, 3(1), 37–45.
Galbraith, J. R. (2014). Organization design challenges resulting from big data. Journal of Organization Design, 3(1), 2–13.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.
Reips, U. D., & Garaizar, P. (2011). Mining twitter: A source for psychological wisdom of the crowds. Behavior Research Methods, 43(3), 635–642.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 61–67.
Fan, W., & Bifet, A. (2013). Mining big data: Current status, and forecast to the future. ACM SIGKDD Explorations Newsletter, 14(2), 1–5.
Schroeck, M., Smart, R., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of big data: How innovative enterprises extract value from uncertain data. Cambridge: IBM Institute for Business Value.
Vossen, G. (2014). Big data as the new enabler in business and other intelligence. Journal of Computer Science, 1(1), 3–14.
Geethakumari, G., & Srivatsava, A. (2012). Big data analysis for implementation of enterprise data security. IRACST-International Journal of Computer Science, Information Technology and Security (IJCSITS), 2(4), 742–746.
Rajaraman, A., & Ullman, J. D. (2012). Mining of massive datasets. Cambridge: Cambridge University Press.
Begoli, E., Horey, J. (2012) Design principles for effective knowledge discovery from big data, software architecture (WICSA) and European conference on software architecture (ECSA) 2012 (pp. 215–218).
Nardo, M., Petracco Giudici, M., & Naltsidis, M. (2015). Walking down wall street with a tablet: A survey of stock market predictions using the web. Journal of Economic Surveys. doi:10.1111/joes.12102.
Acknowledgements
This research was supported by the National Social Science Foundation of China (No.14AGL023). The work of Su Hu was jointly supported by the MOST Program of International S&T Cooperation (Grant No. 2016YFE0123200), National Natural Science Foundation of China (Grant No. 61471100/61101090/61571082), Science and Technology on Electronic Information Control Laboratory (Grant No. 162105003) and Fundamental Research Funds for the Central Universities (Grant No. ZYGX2015J012/ZYGX2014Z005). We would like to thank all the reviewers for their kind suggestions to this work.
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Shasha Liu and Bingjia Shao contribute the idea and the main algorithm of this paper, Yuan Gao and Su Hu perform the simulation of this manuscript. Weigui Zhou and Yi Li help improve the idea and writing of this work.
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Liu, S., Shao, B., Gao, Y. et al. Game theoretic approach of a novel decision policy for customers based on big data. Electron Commer Res 18, 225–240 (2018). https://doi.org/10.1007/s10660-017-9259-6
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DOI: https://doi.org/10.1007/s10660-017-9259-6