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Extracting knowledge of customers’ preferences in massively multiplayer online role playing games

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Abstract

Due to fierce competition in game markets, to identify customers’ true needs is one of the crucial factors in online game industry. Traditionally, game producers heavily rely on game testers, who are primarily responsible for analyzing computer games, finding software defects and being a part of quality control process, to achieve this goal. But, it is not often reliable. To ensure the investment can be returned, game producers need an effective approach to discover frequently shifted customer preferences in time. Recently, Kano model and data mining techniques have been successfully applied to recognize customers’ preferences and implement customer relationship management tasks, respectively. However, in traditional Kano analysis, only basically statistical analysis techniques are used, and they are insufficient to provide advanced knowledge to enterprisers. Therefore, in order to discover the relationship between/among quality elements in Kano model and to extract knowledge related to customer preferences, this study proposes a knowledge acquisition scheme that integrates several data mining techniques including association rule discovery, decision tree, and self-organizing map neural network, into traditional Kano model. An actual case of customer satisfaction survey regarding massively multiplayer online role playing game has been provided to demonstrate the effectiveness of our proposed scheme.

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References

  1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD international conference on management of data, vol 22, pp 207–216

  2. Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In: Proceedings of 20th international conference on very large data bases, pp 487–499

  3. Bose I, Mahapatra RK (2001) Business data mining—a machine learning perspective. Inf Manag 39:211–225

    Article  Google Scholar 

  4. Bortolan G, Pedrycz W (2002) Fuzzy descriptive models: an interactive framework of information granulation. IEEE Trans Fuzzy Syst 10(6):743–755

    Article  Google Scholar 

  5. Carrier CG, Povel O (2003) Characterising data mining software. Intell Data Anal 7:181–192

    Google Scholar 

  6. Chan CCH (2005) Online auction customer segmentation using a neural network model. Int J Appl Sci Eng 3(2):101–109

    Google Scholar 

  7. Chan CCH (2008) Intelligent value-based customer segmentation method for campaign management: a case study of automobile retailer. Expert Syst Appl 34(4):2754–2762

    Article  Google Scholar 

  8. Chen CC, Chuang MC (2008) Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design. Int J Prod Econ 114(2):667–681

    Article  Google Scholar 

  9. Chen LS, Hsu FH, Chen MC, Hsu YC (2008) Developing recommender systems with the consideration of product profitability for sellers. Inf Sci 178(4):1032–1048

    Article  Google Scholar 

  10. Chen LS, Hsu CC, Chen MC (2009) Customer segmentation and classification from blogs by using data mining: an example of VOIP phone. Cybern Syst 40(7):608–632

    Article  Google Scholar 

  11. Chen LS, Liu CH, Hsu CC, Lin CS (2010) C-Kano model: a new approach for discovering attractive quality elements. Total Qual Manag Bus Excell 21(11):1189–1214

    Article  Google Scholar 

  12. Chen RY (2009) A problem-solving approach to product design using decision tree induction based on intuitionistic fuzzy. Eur J Oper Res 196:266–272

    Article  MATH  Google Scholar 

  13. Chen YH, Su CT (2006) A Kano-CKM model for customer knowledge discovery. Total Qual Manag 17(5):589–608

    Article  Google Scholar 

  14. Guh RS, Shiue YR (2008) An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts. Comput Ind Eng 55(2):475–493

    Article  Google Scholar 

  15. Gustafsson A, Ekdahl F, Edvardsson B (1999) Customer focused service development in practice: a case study at Scandinavian airlines system (SAS). Int J Serv Manag 10(4):344–358

    Article  Google Scholar 

  16. Ha SH (2007) Applying knowledge engineering techniques to customer analysis in the service industry. Adv Eng Inform 21(3):293–301

    Article  Google Scholar 

  17. Han J, Kamber M (2001) Data mining: concepts and techniques. Morgan Kaufmann, San Manteo

    Google Scholar 

  18. Hastings EJ, Guha RK, Stanley KO (2009) Automatic content generation in the galactic arms race video game. IEEE Trans Comput Intell AI Games 1(4):245–263

    Article  Google Scholar 

  19. Herzberg F, Mausner B, Snyderman B (1959) The motivation to work, 2nd edn. Wiley, New York

    Google Scholar 

  20. Huiskonen J, Pirttila T (1998) Sharpening logistics customer service strategy planning by applying Kano’s quality element classification. Int J Prod Econ 56–57:253–260

    Article  Google Scholar 

  21. Ishikawa K (1990) Introduction to quality control. 3A Corporation Ltd, Tokyo

    Google Scholar 

  22. Kano N, Takahashi F (1979) Nippon QC Gakkai: 9th annual presentation meeting, pp 21–26

  23. Kano N, Seraku N, Takahashi F, Tsuji S (1984) Attractive quality and must-be quality. J Jpn Soc Qual Control 14:39–48

    Google Scholar 

  24. Kim J, Lee B, Shaw M, Chang H, Nelson M (2001) Application of decision-tree induction techniques to personalized advertisements on internet storefronts. Int J Electron Commerce 5(3):45–62

    Google Scholar 

  25. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480

    Article  Google Scholar 

  26. Kukolj D, Atlagic B, Petrov M (2006) Data clustering using a reorganizing neural network. Cybern Syst 37(7):779–790

    Article  MATH  Google Scholar 

  27. Kuo YF (2004) Integrating Kano’s model into web-community service quality. Total Qual Manag 15(7):925–939

    Article  Google Scholar 

  28. Lee SC, Suh YH, Kim JK, Lee KJ (2004) A cross-national market segmentation of online game industry using SOM. Expert Syst Appl 27(4):559–570

    Article  Google Scholar 

  29. Lee YC, Sheu LC, Tsou YG (2008) Quality function deployment implementation based on Fuzzy Kano model: an application in PLM system. Comput Ind Eng 55:48–63

    Article  Google Scholar 

  30. Li SG, Kuo X (2007) The enhanced quality function deployment for developing virtual items in massive multiplayer online role playing games. Comput Ind Eng 53:628–641

    Article  Google Scholar 

  31. Li ST, Kuo SC (2008) Knowledge discovery in financial investment for forecasting and trading strategy through wavelet-based SOM networks. Expert Syst Appl 34(2):935–951

    Article  MathSciNet  Google Scholar 

  32. Liapis A, Yannakakis GN, Togelius J (2011) Neuroevolutionary constrained optimization for content creation. In: Proceedings of 2011 IEEE conference on computational intelligence and games, pp 71–78

  33. Matzler K, Hinterhuber HH (1998) How to make product development projects more successful by integrating Kano’s model of customer satisfaction into quality function deployment. Technovation 18(1):25–38

    Article  Google Scholar 

  34. Metwally A, Agrawal D, Abbadi AE (2005) Using association rules for fraud detection in web advertising networks. In: Proceedings of VLDB international conference on very large data bases, pp 169–180

  35. Ngai EWT, Xiu L, Chau DCK (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2):2592–2602

    Article  Google Scholar 

  36. Park JS, Chen MS, Yu PS (1995) An effective hash-based algorithm for mining association rules. In: Proceedings of ACM SIGMOD international conference on management of data 24(16), 175–186

  37. Pedersen C, Togelius J, Yannakakis GN (2010) Modeling player experience for content creation. IEEE Trans Comput Intell AI Games 2(1):54–67

    Article  Google Scholar 

  38. Potočnik P, Berlec T, Starbek M, Govekar E (2012) Self-organizing neural network-based clustering and organization of production cells. Neural Comput Appl. doi:10.1007/s00521-012-0938-x

    Google Scholar 

  39. Petrov N, Georgieva A, Jordanov I (2012) Self-organizing maps for texture classification. Neural Comput Appl. doi:10.1007/s00521-011-0797-x

    Google Scholar 

  40. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

    Google Scholar 

  41. Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Manteo

    Google Scholar 

  42. Rahman MKM, Wang PY, Chow TWS, Wu S (2007) A flexible multi-layer self-organizing map for generic processing of tree-structured data. Pattern Recogn 40(5):1406–1424

    Article  MATH  Google Scholar 

  43. Salas R, Moreno S, Allende H, Moraga C (2007) A robust and flexible model of hierarchical self-organizing maps for non-stationary environments. Neurocomputing 70(16–18):2744–2757

    Article  Google Scholar 

  44. Savasee A, Omiecinski E, Navathe S (1995) An efficient algorithm for mining association rules in large databases. In: Proceedings of 21st very large data bases conference, pp 432–444

  45. Senabre C, Velasco E, Valero S (2011) Self-organizing maps for identification of tire model longitudinal braking parameters of a vehicle on a roller brake tester and on flat ground. Neural Comput Appl. doi:10.1007/s00521-011-0666-7

    Google Scholar 

  46. Sikder IU, Munakata T (2009) Application of rough set and decision tree for characterization of premonitory factors of low seismic activity. Expert Syst Appl 36(1):102–110

    Article  Google Scholar 

  47. Strategy Analytics (2007) Online games to generate one-third of game revenue by 2011. http://www.marketingcharts.com/interactive/online-games-to-generate-one-third-of-game-revenue-by-2011-1736/

  48. Swan JE, Combs LJ (1976) Product performance and consumer satisfaction: a new concept. J Mark 40(2):25–33

    Article  Google Scholar 

  49. Tan KC, Pawitra TA (2001) Integrating SERVQUAL and Kano’s model into QFD for service excellence development. Manag Serv Qual 11(6):418–430

    Article  Google Scholar 

  50. Wang CH (2009) Outlier identification and market segmentation using kernel-based clustering techniques. Expert Syst Appl 36(2):3744–3750

    Article  Google Scholar 

  51. Weihrich H (1994) Quality: the imperative, the jungle, and the two-factor theory. Ind Manag 36(4):19

    Google Scholar 

  52. Witell L, Lofgren M (2007) Classification of quality attributes. Manag Serv Qual 17(1):54–73

    Article  Google Scholar 

  53. Xu QL, Jiao RJ, Yang X, Helander MG, Khalid HM, Anders O (2007) Customer requirement analysis based on an analytical Kano model. In: Proceedings of 2007 IEEE international conference on industrial engineering and engineering management, 2–4 Dec, pp 1287–1291

  54. Zeitouni K, Chelghoum N (2001) Spatial decision tree-application to traffic risk analysis. In: Proceedings of the ACS/IEEE international conference on computer systems and applications, p 203

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Acknowledgments

This work was supported in part by National Science Council of Taiwan (Grant No. NSC 98-2410-H-324-007-MY2).

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  1. Department of Information Management, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong District, Taichung City, 41349, Taiwan

    Long-Sheng Chen & Pao-Chung Chang

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  1. Long-Sheng Chen
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Correspondence to Long-Sheng Chen.

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Chen, LS., Chang, PC. Extracting knowledge of customers’ preferences in massively multiplayer online role playing games. Neural Comput & Applic 23, 1787–1799 (2013). https://doi.org/10.1007/s00521-012-1145-5

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