Title :
Online Shopping Preference Analysis of Campus Network Users Based on MapReduce
Author :
Yang Junchao ; Luo Jiangtao ; Shen Jian ; Deng Shengxiong
Author_Institution :
Key Lab. of Commun. Networks & Testing Technol., Chongqing Univ. of Posts & Telecommun., Chongqing, China
Abstract :
User online shopping preference mining is the key point on user found, e-commerce marketing and user personalized recommendation. A method for Online shopping preference analysis based on MapReduce is proposed in this paper. The campus network traffic is analyzed using MapReduce model, in which the features of user online shopping behavior are extracted by four MapReduce jobs using deep packet inspection (DPI). Making use of those features occuring to different e-commerce websites and with the help of the product information database established by a web crawler, user preference of e-commerce websites and categories of purchased product are analyzed. User conversion rates of three e-commerce websites(Taobao, Tmall, JD) are presented.
Keywords :
Web sites; data handling; data mining; electronic commerce; marketing data processing; parallel processing; DPI; JD; MapReduce model; Taobao; Tmall; Web crawler; campus network traffic; campus network users; deep packet inspection; e-commerce Web sites; e-commerce marketing; feature extraction; online shopping preference analysis; user conversion rates; user online shopping behavior; user online shopping preference mining; user personalized recommendation; user preference; Business; Crawlers; Data mining; Databases; Feature extraction; Inspection; Telecommunication traffic; MapReduce; campus network; deep packet inspection; online shopping; preference analysis;
Conference_Titel :
Cloud Computing and Big Data (CCBD), 2014 International Conference on
DOI :
10.1109/CCBD.2014.12
