  Most companies utilize demographic information to develop their strategy in a
market. However, such information is not available to most retail companies.
Several studies have been conducted to predict the demographic attributes of
users from their transaction histories, but they have some limitations. First,
they focused on parameter sharing to predict all attributes but capturing
task-specific features is also important in multi-task learning. Second, they
assumed that all transactions are equally important in predicting demographic
attributes. However, some transactions are more useful than others for
predicting a certain attribute. Furthermore, decision making process of models
cannot be interpreted as they work in a black-box manner. To address the
limitations, we propose an Embedding Transformation Network with Attention
(ETNA) model which shares representations at the bottom of the model structure
and transforms them to task-specific representations using a simple linear
transformation method. In addition, we can obtain more informative transactions
for predicting certain attributes using the attention mechanism. The
experimental results show that our model outperforms the previous models on all
tasks. In our qualitative analysis, we show the visualization of attention
weights, which provides business managers with some useful insights.
