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首页> 外文期刊>Malaysian Journal of Computer Science >An Artificial Neural Network Classification Approach For Improving Accuracy Of Customer Identification In E-Commerce
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An Artificial Neural Network Classification Approach For Improving Accuracy Of Customer Identification In E-Commerce

机译：人工神经网络分类方法提高电子商务中客户识别的准确性

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摘要

With the advancesin Web-based oriented technologies, experts are able to capture user activities on the Web. Users’ Web browsing behavior is used for user identification. Identifying users during their activities is extremely important in electronic commerce (e-Commerce)as it has the potential to prevent illegal transactions or activities particularly for users who enter the system through the use of unknown methods.In addition, customer behavioral pattern identification provides a wide spectrum of applications such as personalized Web pages, product recommendations and present advertisements. In this research, a framework for users’ behavioral profiling formation is presented and customer behavioral patternsare used for customer identification in the e-Commerce environment. Based on activity control, policies such as user restriction or blockingcan be applied.The neural network classification and the measure of similarity among behavioral patterns are two approaches applied in this research. The results of multi-layer perceptron with a back propagation learning algorithm indicate that there is less error and up to 15.12% more accuracy on average.The results imply that the accuracy of the neural network approach in customer pattern behavior recognition increases when the number of customers grows.In contrast, the accuracy of the similarity of pattern method decreases.
机译：随着基于Web的技术的进步，专家们可以捕获Web上的用户活动。用户的Web浏览行为用于识别用户。识别用户活动期间的行为在电子商务（e-Commerce）中极为重要，因为它有可能防止非法交易或活动，特别是对于通过使用未知方法进入系统的用户而言。此外，客户行为模式识别还提供了广泛的应用程序，例如个性化网页，产品推荐和当前广告。在这项研究中，提出了一种用于用户行为概况分析的框架，并且将客户行为模式用于电子商务环境中的客户识别。基于活动控制，可以应用诸如用户限制或阻止等策略。神经网络分类和行为模式之间的相似性度量是本文研究的两种方法。带有反向传播学习算法的多层感知器的结果表明，错误更少，平均准确率提高了15.12％。结果表明，随着客户数量行为的增加，神经网络方法在客户模式行为识别中的准确性会提高。客户增长。相反，模式方法相似度的准确性降低。

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