Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CCBD.2014.12 | CCBD |
Keywords | Field | DocType |
web sites,parallel processing,user online shopping preference mining,deep packet inspection,mapreduce,dpi,user personalized recommendation,jd,taobao,mapreduce, deep packet inspection, campus network, online shopping, preference analysis,e-commerce marketing,e-commerce web sites,campus network,online shopping,mapreduce model,preference analysis,marketing data processing,feature extraction,user conversion rates,user preference,campus network traffic,data handling,tmall,data mining,campus network users,online shopping preference analysis,electronic commerce,user online shopping behavior,web crawler,inspection,databases,business | Deep packet inspection,World Wide Web,Campus network,Computer science,Feature extraction,Web crawler | Conference |
ISSN | Citations | PageRank |
2378-3680 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Junchao | 1 | 0 | 0.34 |
| Jiangtao Luo | 2 | 6 | 3.50 |
| Shen Jian | 3 | 0 | 0.34 |
| Shengxiong Deng | 4 | 1 | 0.68 |