ABSTRACT In order to measure the similarity of non-equal length and non-numerical sequence effectively, in this paper, the access sequence similarity calculation method was proposed based on the characteristics of e-commerce user access sequence. The sliding window method was improved by increasing the similarity calculation of nodes and optimizing the sliding similarity calculation method. The key factor of Edit Distance on Real Sequences was optimized. It mainly includes the calculation method of increasing the similarity of nodes and optimizing the calculation method of sliding similarity; the calculation method of subcost in the editing distance of real sequences is optimized. Then, the optimized Edit Distance on Real Sequences was embedded into the improved sliding window method to replace the original distance calculation method. Based on the access sequence similarity calculation results, the clustering algorithm was used to get the e-commerce users type. The experimental results showed the following facts: The improved access sequence similarity algorithm can measure the similarity of non-numerical and non-equal length sequences more accurately; based on the similarity of access sequences, it is possible to divide the types of e-commerce users more effectively, besides the e-commerce users are mainly composed of young men, users’ online time shows obvious fragmentation characteristics, their online browsing behavior obeys long tail distribution, they still primarily buy hot items, and the e-commerce users can be divided into six categories.

中文翻译：

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基于访问序列相似度的电子商务用户类型识别

摘要 为了有效衡量非等长非数字序列的相似度，本文根据电子商务用户访问序列的特点，提出了访问序列相似度计算方法。通过增加节点的相似度计算，优化滑动相似度计算方法，对滑动窗口法进行了改进。优化了真实序列编辑距离的关键因素。主要包括增加节点相似度的计算方法和优化滑动相似度的计算方法；优化了真实序列编辑距离中subcost的计算方法。然后，将优化后的真实序列上的编辑距离嵌入到改进​​的滑动窗口方法中，以取代原来的距离计算方法。基于访问序列相似度计算结果，采用聚类算法得到电子商务用户类型。实验结果表明：改进后的访问序列相似度算法可以更准确地度量非数字非等长序列的相似度；基于访问序列的相似性，可以更有效地划分电商用户类型，除电商用户以年轻男性为主外，用户在线时间呈现明显的碎片化特征，在线浏览行为服从长尾分布，仍以购买热点商品为主，电商用户可分为六类。实验结果表明：改进后的访问序列相似度算法可以更准确地度量非数字非等长序列的相似度；基于访问序列的相似性，可以更有效地划分电商用户类型，除电商用户以年轻男性为主外，用户在线时间呈现明显的碎片化特征，在线浏览行为服从长尾分布，仍以购买热点商品为主，电商用户可分为六类。实验结果表明：改进后的访问序列相似度算法可以更准确地度量非数字非等长序列的相似度；基于访问序列的相似性，可以更有效地划分电商用户类型，除电商用户以年轻男性为主外，用户在线时间呈现明显的碎片化特征，在线浏览行为服从长尾分布，仍以购买热点商品为主，电商用户可分为六类。