[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.
[1] 中国互联网信息中心. 第33次中国互联网络发展状况统计报告[R/OL]. [2014-03-05]. http://www.cnnic.net.cn/ hlwfzyj/hlwxzbg/hlwtjbg/201403/t20140305_46240.htm. (China Internet Network Information Center. Statistical Report of the 33rd Chinese Internet Development [R/OL]. [2014-03-05]. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/ 201403/t20140305_46240.htm.)
[2] Rohm A J, Swaminathan V. A Typology of Online Shoppers Based on Shopping Motivations [J]. Journal of Business Research, 2004, 57 (7): 748-757.
[3] Pahnila S, Warsta J. Online Shopping Viewed from a Habit and Value Perspective [J]. Behaviour & Information Technology, 2010, 29(6): 621-632.
[4] Prasad P, Malik L G. Generating Customer Profiles for Retail Stores Using Clustering Techniques [J]. International Journal on Computer Science and Engineering, 2011, 3(6): 2506-2510.
[5] 王义, 马尚才. 基于用户行为的个性化推荐系统的设计与应用[J]. 计算机系统应用, 2010, 19(8): 29-33. (Wang Yi, Ma Shangcai. Design and Application of Personalized Recommendation System Based on Users Behavior [J]. Computer Systems & Applications, 2010, 19(8): 29-33.)
[6] 王微微, 夏秀峰, 李晓明. 一种基于用户行为的兴趣度模型[J]. 计算机工程与应用, 2012, 48(8): 148-151. (Wang Weiwei, Xia Xiufeng, Li Xiaoming. Personal Interest Degree Model Based on Consumer Behavior [J]. Computer Engineering and Applications, 2012, 48(8): 148-151.)
[7] Moe W W. Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream [J]. Journal of Consumer Psychology, 2003, 13(1-2): 29-39.
[8] 正望咨询公司. 2012年网上消费者调查报告[R/OL]. [2012- 05-02]. http://tech.sina.com.cn/z/zhengwang2012/. (China IntelliConsulting Corp. The Report of 2012 Online Consu-mers [R/OL]. [2012-05-02]. http://tech.sina.com.cn/z/ zhengwang2012/.)
[9] Engel J F, Kollat D T, Blackwell R D. Consumer Behavior [M]. New York: Holt, Rinehart & Winston, 1968: 10-25.
[10] 姚秀丽. 消费者行为及网络购物[M]. 北京: 科学出版社, 2010: 31. (Yao Xiuli. Consumer Behavior and Online Shopping [M]. Beijing: Science Press, 2010: 31.)
[11] Dervin B. From the Mind's Eye of the User: The Sense- making Qualitative-quantitative Methodology [M]. Englewood, CA, USA: Libraries Unlimited, 1992: 327-338.
[12] Belkin N J. Anomalous States of Knowledge as Basis for Information Retrieval [J]. The Canadian Journal of Information Science, 1980(5): 133-143.
[13] Wilson T D. Models in Information Behaviour Research [J]. Journal of Documentation, 1999(3): 249-270.
