[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.
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