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数据分析与知识发现  2018, Vol. 2 Issue (6): 79-91     https://doi.org/10.11925/infotech.2096-3467.2018.0101
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于复杂网络重叠社区的电子商务用户复合类型识别*
钱晓东(), 李敏
兰州交通大学经济管理学院 兰州 730070
Identifying E-commerce User Types Based on Complex Network Overlapping Community
Qian Xiaodong(), Li Min
School of Economics and Management, Lanzhou Jiaotong University, Lanzhou 730070, China
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摘要 

目的】由用户特征的多样性可知, 用户往往是多重角色的混合体, 而已有研究很少涉及用户复合类型, 这不利于电子商务运营商全面地了解客户。本文提出一种电子商务用户复合类型的识别算法, 为运营商的个性化营销提供数量化依据。【方法】基于用户访问序列的特点构建节点距离矩阵; 从重定义匹配数、编辑代价和编辑规则等方面改进Jaro-Winkler Distance算法, 计算用户访问序列距离矩阵; 以距离矩阵为基础, 区分中心用户和非中心用户, 并构建用于用户复合类型识别的复杂网络; 从改进初始模块度增量矩阵等方面改进CNM算法, 获得用户类型初始划分; 再利用模糊隶属函数进行用户优化, 最终得到电子商务用户复合类型。【结果】以CONGA算法作为比较基准, 首先采用LFR基准程序生成的网络测试本文算法性能, 计算结果表明本文算法的NMI值较基准算法最高提高了15.60%; 再利用用户真实在线数据进行算法应用, 计算结果表明本文算法的整体聚类系数值较基准算法最高提高了10.87%; 且算法的时间复杂度低于基准算法。【局限】本文算法需要主观设定三个参数。【结论】用户网络符合小世界模型特性, 具有复杂网络的典型形态; 利用本文算法可以有效识别电子商务用户复合类型。

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作者相关文章
钱晓东
李敏
关键词 用户复合类型复杂网络重叠社区访问序列距离CNM模糊隶属函数    
Abstract

[Objective] This paper presents an algorithm to identify composite types of e-commerce users, aiming to improve e-commerce operators’ personalized marketing services. [Methods] First, we built the node distance matrix based on the characteristics of user access sequences. Then, we modified the Jaro-Winkler distance algorithm from the perspectives of redefining matching number, editing cost and rules. Third, we used the improved algorithm to calculate the user access sequence distance matrix. Based on the distance matrix, we distinguished the central and non-central users to construct a complex network for identifying user composite types. We used the improved CNM algorithm to obtain the initial user types. With the help of fuzzy membership function for user optimization, we obtained their composite types. [Results] Compared to CONGA, the NMI of the proposed algorithm was improved by 15.60%. The algorithm was also applied to examine the real user’s online data, and its overall clustering coefficient was 10.87% higher than the CONGA. The time complexity of the new algorithm was reduced too. [Limitations] The proposed algorithm needs to set three parameters subjectively. [Conclusions] The user network conforms to the characteristics of a small-world model and has the typical morphology of a complex network. The algorithm can effectively identify the composite types of e-commerce users.

Key wordsUser Composite Type    Complex Network    Overlapping Communities    Access Sequence Distance    CNM    Membership Function
收稿日期: 2018-01-25      出版日期: 2018-07-11
ZTFLH:  TP393  
基金资助:*本文系国家自然科学基金项目“基于复杂网络的商务大数据聚类与管理应用研究”(项目编号: 71461017)的研究成果之一
引用本文:   
钱晓东, 李敏. 基于复杂网络重叠社区的电子商务用户复合类型识别*[J]. 数据分析与知识发现, 2018, 2(6): 79-91.
Qian Xiaodong,Li Min. Identifying E-commerce User Types Based on Complex Network Overlapping Community. Data Analysis and Knowledge Discovery, 2018, 2(6): 79-91.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0101      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I6/79
  改进的Jaro-Winkler Distance计算示例
η bt l p m' Ψ ξ dj dw dsf dsc
2/5 2/5 2 0.1 10 6 9 1 1 0.820 0.856
3/5 3/5 2 0.2 10 6 9 1 1 0.820 0.892
4/5 4/5 3 0.2 9 5 8 0.967 0.987 0.678 0.871
4/5 4/5 2 0.1 9 5 8 0.967 0.973 0.678 0.742
  不同需求条件下用户距离计算结果
  p取值1-100时${{\delta }_{cv}}$的取值
  p取值1-9时${{\delta }_{cv}}$的取值
  基于复杂网络重叠社区划分的电子商务用户复合类型识别模型
网络编号 n μ k 重叠
节点数
节点可归属
社区个数nbc
1 10 000 0.2 12.5143 20 4
2 10 000 0.2 12.6180 40 5
3 10 000 0.2 12.2637 60 6
4 10 000 0.2 12.5192 80 7
5 10 000 0.2 12.4001 100 8
6 10 000 0.2 12.3052 120 9
7 10 000 0.2 12.4211 140 10
1’ 10 000 0.3 12.5936 20 4
2’ 10 000 0.3 12.4137 40 5
3’ 10 000 0.3 12.3665 60 6
4’ 10 000 0.3 12.7131 80 7
5’ 10 000 0.3 12.2684 100 8
6’ 10 000 0.3 12.0958 120 9
7’ 10 000 0.3 12.2384 140 10
  LFR生成的基准网络
  η=med(disnode), bt=med(dsf), ε=0.00001
  η=mod(disnode), bt=mod(dsf), ε=0.00001
  η=med(disnode), bt=med(dsf), ε=0.001
  η=mod(disnode), bt=mod(dsf), ε=0.001
  η=med(disnode), bt=med(dsf)
  η=mod(disnode), bt=mod(dsf)
  η=med(disnode), bt=med(dsf), ε=0.00001
  η=mod(disnode), bt=mod(dsf), ε=0.00001
  η=med(disnode), bt=med(dsf), ε=0.001
  η=mod(disnode), bt=mod(dsf), ε=0.001
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