[Objective] It's vital to detect the consumers' shopping needs in the e-commerce environment by mining clickstream logs so as to achieve effective shopping guidance. [Methods] This paper first marks page types that users visit in Taobao.com, then uses K-means cluster to analyse the visit session data. Two clustering indexes are used, that are page-type and page-complexity. [Results] Based on page types, the visit sessions are clustered to four user need states, including direct management, continuous searching, product browsing and information seeking. The four types are then categorized into nine detailed ones based on page complexity. [Limitations] The effectiveness of the user need state analysis needs to be further validated in real-world environment. [Conclusions] It is an effecitve and operable method to detect and denote the e-shopper's need states by clustering analysis of the visiting sessions.
张文君, 王军, 徐山川. 电商用户需求状态的聚类分析——以淘宝网女装为例[J]. 现代图书情报技术, 2015, 31(3): 67-74.
Zhang Wenjun, Wang Jun, Xu Shanchuan. The Probing of E-commerce User Need States by Page Cluster Analysis ——An Empirical Study on Women's Clothes from Taobao.com. New Technology of Library and Information Service, 2015, 31(3): 67-74.
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