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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/bf01478770

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ISSN

1433-3058

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Extracting knowledge of customers’ preferences in

Authors: LongSheng Chen PaoChung Chang
Publish Date: 2012/09/14
Volume: 23, Issue: 6, Pages: 1787-1799
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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 selforganizing 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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