For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed to identify lead users efficiently based on the cost-sensitive learning and support vector machine theory. To this end, the characteristics of lead users in CCPI are first analyzed and concluded in-depth. On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in CCPI is further proposed. A real case is provided to illustrate the effectiveness and advantages of the ICS-SVM method on lead user identification in CCPI. The case results show that the ICS-SVM method can effectively identify lead users in CCPI. This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI.

中文翻译：

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客户协同产品创新中潜在用户识别的智能方法

对于客户协作产品创新（CCPI），主要用户是产品创新的强大推动者。识别主要用户对于成功实施CCPI至关重要。为了克服传统评估方法的不足，提出了一种基于成本敏感型学习和支持向量机理论的有效识别潜在用户的智能方法。为此，首先对CCPI中主要用户的特征进行了分析和深入总结。在此基础上，考虑样本误识别成本和识别准确率，提出了一种改进的成本敏感型学习支持向量机（ICS-SVM）方法，用于CCPI中的潜在用户识别。提供了一个实际案例来说明ICS-SVM方法在CCPI中标识潜在用户方面的有效性和优势。案例结果表明，ICS-SVM方法可以有效地识别CCPI中的主要用户。这项工作提出了一种识别高价值潜在用户的新方法，为用户创新文献做出了贡献，并为CCPI中的有效用户管理提供了决策支持。