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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (6): 79-91    DOI: 10.11925/infotech.2096-3467.2018.0101
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Identifying E-commerce User Types Based on Complex Network Overlapping Community
Xiaodong Qian(),Min Li
School of Economics and Management, Lanzhou Jiaotong University, Lanzhou 730070, China
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[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     
Received: 25 January 2018      Published: 11 July 2018

Cite this article:

Xiaodong Qian,Min Li. Identifying E-commerce User Types Based on Complex Network Overlapping Community. Data Analysis and Knowledge Discovery, 2018, 2(6): 79-91.

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[1] 中国社会科学院财经战略研究院, 中央电视台财经频道. 中国电子商务半年报(2017)[EB/OL]. [2017-07-22]. .
[1] (National Academy of Economic Strategy, CCTV Finance and Economics. China Electronic Commerce Semi-Annual Report [EB/OL]. [2017-07-22].
[2] 中国互联网络信息中心.第40次中国互联网络发展状况统计报告[EB/OL]. [2017-08-17]. .
[2] (China Internet Network Information Center. The 40th China Statistical Report on Internet Development[EB/OL]. [2017-08-17].
[3] Suh E H, Noh K C, Suh C K.Customer List Segmentation Using the Combined Response Model[J]. Expert Systems with Applications, 1999, 17(2): 89-97.
[4] Heilman C M, Bowman D.Segmenting Consumers Using Multiple-category Purchase Data[J]. International Journal of Research in Marketing, 2002, 19(3): 225-252.
[5] 徐翔斌, 王佳强, 涂欢, 等. 基于改进RFM模型的电子商务客户细分[J]. 计算机应用, 2012, 32(5): 1439-1442.
[5] (Xu Xiangbin, Wang Jiaqiang, Tu Huan, et al.Customer Classification of E-commerce Based on Improved RFM Model[J]. Journal of Computer Applications, 2012, 32(5): 1439-1442.)
[6] Gregory S.An Algorithm to Find Overlapping Community Structure in Networks[C]//Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery. Berlin, Heidelberg: Springer, 2007: 91-102. DOI: 10.1007/978-3-540-74976-9_12.
[7] 刘功申, 孟魁, 郭弘毅, 等. 基于贡献函数的重叠社区划分算法[J]. 电子与信息学报, 2017, 39(8): 1964-1971.
[7] (Liu Gongshen, Meng Kui, Guo Hongyi, et al.Overlapping- communities Recognition Algorithm Based on Contribution Function[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1964-1971.)
[8] 刘世超, 朱福喜, 甘琳. 基于标签传播概率的重叠社区发现算法[J]. 计算机学报, 2016, 39(4): 717-729.
[8] (Liu Shichao, Zhu Fuxi, Gan Lin.A Label-Propagation-Probability-Based Algorithm for Overlapping Community Detection[J]. Chinese Journal of Computers, 2016, 39(4): 717-729.)
[9] 姜雅文, 贾彩燕, 于剑. 基于类原型的复杂网络重叠社区发现方法[J]. 模式识别与人工智能, 2013, 26(7): 648-659.
[9] (Jiang Yawen, Jia Caiyan, Yu Jian.Overlapping Community Detection in Complex Networks Based on Cluster Prototypes[J]. PR & AI, 2013, 26(7): 648-659.)
[10] Majorek K A, Dunin-Horkawicz S, Steczkiewicz K, et al.The RNase H-like Superfamily: New Members, Comparative Structural Analysis and Evolutionary Classification[J]. Nucleic Acids Research, 2014, 42(7): 4160-4179.
[11] Paleo B W.An Approximate Gazetteer for GATE Based on Levenshtein Distance[C]//Proceedings of Student Session of the European Summer School of Logic, Language and Information. 2007.
[12] Boytsov L. Indexing Methods for Approximate Dictionary Searching: Comparative Analysis[J]. Journal of Experimental Algorithmics, 2011, 16: Article No. 1.1.
[13] Winkler W E.String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage[C]//Proceedings of the Section on Survey Research. American Statistical Association, 1990: 354-359.
[14] Jaro M A.Advances in Record-Linkage Methodology as Applied to Matching the 1985 Census of Tampa, Florida[J]. Journal of the American Statistical Association, 1989, 84(406): 414-420.
[15] Rodriguez A, Laio A.Clustering by Fast Search and Find of Density Peaks[J]. Science, 2014, 344(6191): 1492-1496.
[16] Newman M E J. Fast Algorithm for Detecting Community Structure in Networks[J]. Physical Review E: Statistical Nonlinear & Soft Matter Physics, 2003, 69(6 Pt 2): 066133.
[17] Nicosia V, Mangioni G, Carchiolo V, et al.Extending the Definition of Modularity to Directed Graphs with Overlapping Communities[J]. Journal of Statistical Mechanics Theory & Experiment, 2009(3): 3166-3168.
[18] Lancichinetti A, Fortunato S, Radicchi F.Bechmark Graghs for Testing Community Detection Algorithm[J]. Physical Review E, 2008, 78(4): 046110.
[19] Estévez P A, Tesmer M, Perez C A, et al.Normalized Mutual Information Feature Selection[J]. IEEE Transactions on Neural Networks, 2009, 20(2): 189-201.
[20] Saramäki J, Kivelä M, Onnela J P, et al.Generalizations of the Clustering Coefficient to Weighted Complex Networks[J]. Physical Review E: Statistical Nonlinear & Soft Matter Physics, 2007, 75(2): 027105.
[21] 乔少杰, 韩楠, 张凯峰, 等. 复杂网络大数据中重叠社区检测算法[J]. 软件学报, 2017, 28(3): 631-647.
[21] (Qiao Shaojie, Han Nan, Zhang Kaifeng, et al.Algorithm for Detecting Overlapping Communities from Complex Network Big Data[J]. Journal of Software, 2017, 28(3): 631-647.)
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