Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (3): 67-74    DOI: 10.11925/infotech.1003-3513.2015.03.09
Current Issue | Archive | Adv Search |
The Probing of E-commerce User Need States by Page Cluster Analysis ——An Empirical Study on Women's Clothes from
Zhang Wenjun, Wang Jun, Xu Shanchuan
Department of Information Management, Peking University, Beijing 100871, China
Download: PDF(1139 KB)   HTML  
Export: BibTeX | EndNote (RIS)      

[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, 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.

Key wordsUser need state      E-commerce      E-shopping behavior      K-means clustering     
Received: 16 September 2014      Published: 16 April 2015
:  TP393  

Cite this article:

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 New Technology of Library and Information Service, 2015, 31(3): 67-74.

URL:     OR

[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]. 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]. 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]. (China Intelli­Consulting Corp. The Report of 2012 Online Consu­-mers [R/OL]. [2012-05-02]. 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.

[1] Xiaofeng Li,Jing Ma,Chi Li,Hengmin Zhu. Identifying Commodity Names Based on XGBoost Model[J]. 数据分析与知识发现, 2019, 3(7): 34-41.
[2] Chuanming Yu,Yajing Guo,Yutian Gong,Manyu Huang,Hufeng Peng. Evolution and Regional Differences of E-commerce Policies for Rural Poverty Reduction Based on Topic over Time Model[J]. 数据分析与知识发现, 2018, 2(7): 34-45.
[3] Xiaoting Jia,Mingyang Wang,Yu Cao. Automatic Abstracting of Chinese Document with Doc2Vec and Improved Clustering Algorithm[J]. 数据分析与知识发现, 2018, 2(2): 86-95.
[4] Yu Wang,Xiuxiu Li. Evaluating Business Reputation with E-Commerce Comments[J]. 数据分析与知识发现, 2017, 1(8): 59-67.
[5] Xueying Wang,Zixuan Zhang,Hao Wang,Sanhong Deng. Evaluating Brands of Agriculture Products: A Literature Review[J]. 数据分析与知识发现, 2017, 1(7): 13-21.
[6] Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[7] Peng Zhu, Xiaoxiao Zhao, Wei Wu. Factors Influencing Mobile E-commerce Consumers’ Preferences: An Empirical Study[J]. 数据分析与知识发现, 2017, 1(3): 1-9.
[8] Liu Honglian,Zhang Pengyi,Wang Jun. Multi-session Product Information Seeking Behaviors, Motivation, and Influencing Factors[J]. 现代图书情报技术, 2016, 32(4): 1-7.
[9] Ren Yuwei, Lv Xueqiang, Li Zhuo, Xu Liping. Named Entity Recognition from Search Log[J]. 现代图书情报技术, 2015, 31(6): 49-56.
[10] Yuan Xingfu, Zhang Pengyi, Wang Jun. “State-Behavior” Modeling and Its Application in Analyzing Product Information Seeking Behavior of E-commerce Websites Users[J]. 现代图书情报技术, 2015, 31(6): 93-100.
[11] Xiao Tianjiu, Liu Ying. Words and N-gram Models Analysis for “A Dream of Red Mansions”[J]. 现代图书情报技术, 2015, 31(4): 50-57.
[12] Wu Wankun, Wu Qinglie, Gu Jinjiang. Hot Topic Extraction from E-commerce Microblog Based on EM-LDA Integrated Model[J]. 现代图书情报技术, 2015, 31(11): 33-40.
[13] Gao Jinsong, Liang Yanqi, Li Ke, Xiao Lian, Zhou Ximan. E-commerce Credit Information Service Model for Linked Data[J]. 现代图书情报技术, 2014, 30(6): 8-16.
[14] Li Hui, Liu Dongsu. E-Commerce Reputation Model Based on Elimination Differences of User Subjective Evaluation[J]. 现代图书情报技术, 2012, 28(2): 48-52.
[15] Wang Dongbo, Han Pu, Shen Si, Wei Xiangqing. Research of Mining the Category Knowledge Based on English-Chinese Humanities and Social Sciences Parallel Corpus in Phrase Level[J]. 现代图书情报技术, 2012, (11): 40-46.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938