Fusing Multifaceted Transaction Data for User Modeling and Demographic Prediction
This work addresses the challenge of obtaining sensitive demographic data for user-centric applications like personalization and identity security, but it appears incremental as it builds on prior methods using purchase history.
The paper tackles the problem of predicting user demographic attributes from transaction data, presenting an embedding-based method that integrates multifaceted sequences and auxiliary relational tables to improve user modeling and prediction accuracy.
Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications. Demographic data is an enabler of personalization, identity security, and other applications. Despite that, this data is sensitive and often hard to obtain. Previous work has shown that purchase history can be used for multi-task prediction of many demographic fields such as gender and marital status. Here we present an embedding based method to integrate multifaceted sequences of transaction data, together with auxiliary relational tables, for better user modeling and demographic prediction.