  In the area of ad-targeting, predicting user responses is essential for many
applications such as Real-Time Bidding (RTB). Many of the features available in
this domain are sparse categorical features. This presents a challenge
especially when the user responses to be predicted are rare, because each
feature will only have very few positive examples. Recently, neural embedding
techniques such as word2vec which learn distributed representations of words
using occurrence statistics in the corpus have been shown to be effective in
many Natural Language Processing tasks. In this paper, we use real-world data
set to show that a similar technique can be used to learn distributed
representations of features from users' web history, and that such
representations can be used to improve the accuracy of commonly used models for
predicting rare user responses.
