  Predicting the click-through rate of an advertisement is a critical component
of online advertising platforms. In sponsored search, the click-through rate
estimates the probability that a displayed advertisement is clicked by a user
after she submits a query to the search engine. Commercial search engines
typically rely on machine learning models trained with a large number of
features to make such predictions. This is inevitably requires a lot of
engineering efforts to define, compute, and select the appropriate features. In
this paper, we propose two novel approaches (one working at character level and
the other working at word level) that use deep convolutional neural networks to
predict the click-through rate of a query-advertisement pair. Specially, the
proposed architectures only consider the textual content appearing in a
query-advertisement pair as input, and produce as output a click-through rate
prediction. By comparing the character-level model with the word-level model,
we show that language representation can be learnt from scratch at character
level when trained on enough data. Through extensive experiments using billions
of query-advertisement pairs of a popular commercial search engine, we
demonstrate that both approaches significantly outperform a baseline model
built on well-selected text features and a state-of-the-art word2vec-based
approach. Finally, by combining the predictions of the deep models introduced
in this study with the prediction of the model in production of the same
commercial search engine, we significantly improve the accuracy and the
calibration of the click-through rate prediction of the production system.
