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.

A river of red-hot lava was caught flowing out from Mount Etna in breathtaking new drone footage. Smoking magma can be see rolling down the snow-topped mountainside as the drone glides in and out of the red-hot flow. The latest outpouring comes just weeks after an eruption forced a BBC reporter and a scientist to flee for their lives. Etna, at 3,330 metres (10,926 feet), is the highest volcano in mainland Europe. The volcano, one of the most active in the world, has been largely dormant for the last two years.