Name
Abstract | ||
|---|---|---|
While e-commerce has grown substantially over last several years, more and more people are utilizing this popular channel to purchase products and services. Thus the ability to predict user demographics, including gender, age and location has important applications in advertising, personalization, and recommendation. In this paper, we aim to automatically predict the users' genders based on their product viewing logs. Our study is based on a dataset from PAKDD'15 data mining competition. We propose an architecture for gender prediction, which consists of the "machine learning model" and the "label updating function". The experimental results show that our proposed method significantly outperform baseline methods. A detailed analysis of features provides an entertaining insight into behavior variation on female and male users. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/WI-IAT.2015.106 | 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) |
Keywords | Field | DocType |
gender predict,e-commerce | Data mining,Architecture,World Wide Web,Information retrieval,Computer science,Support vector machine,Communication channel,Feature extraction,Demographics,E-commerce,Personalization | Conference |
Volume | Citations | PageRank |
3 | 2 | 0.36 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyu Lu | 1 | 2 | 0.36 |
| Meng Zhao | 2 | 2 | 0.36 |
| Hui Zhang | 3 | 3 | 1.73 |
| Chen Zhang | 4 | 53 | 8.94 |
| Wei Wang 0061 | 5 | 2 | 0.36 |
| H. Wang | 6 | 665 | 49.59 |