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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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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     
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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0101     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I6/79

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