Multiple Demographic Attributes Prediction in Mobile and Sensor Devices

Abstract

Users’ real demographic attributes is impressively useful for intelligent marketing, automatic advertising and human-computer interaction. Traditional method on attribute prediction make great effort on the study of social network data, but ignore massive volumes of disparate, dynamic, and temporal data derived from ubiquitous mobile and sensor devices. For example, daily walking step counts produced by pedometer. Multiple demographic prediction on temporal data have two problems. First one is that differential effectiveness of different time period data for prediction is unclear. And another one is how to effectively learn the complementary correlations between different attributes. To address the above problem, we propose a novel model named Correlation-Aware Neural Embedding with Attention (CANEA), which first directly separates different attribute oriented feature using separated embedding layer, and use attention mechanism to assign a higher weight to dominant time point. Then it captures informative correlations using correlation learning layer. Finally we obtain the refined task-specific representations with optimal correlation information for predicting certain attributes. Experimental results show the effectiveness of our method.