Your Cart tells You: Inferring Demographic Attributes from Purchase Data

Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of big data to automatically infer users’ demographic attributes based on their purchase data. Typically, demographic prediction can be formalized as a multi-task multi-class prediction problem, i.e., multiple demographic attributes (e.g., gender, age and income) are to be inferred for each user where each attribute may belong to one of N possible classes (N≥2). Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. Meanwhile, manually defined features require professional knowledge and often ...

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng
• Keywords: Data Mining, WSDM 2016