  Predicting user responses, such as click-through rate and conversion rate,
are critical in many web applications including web search, personalised
recommendation, and online advertising. Different from continuous raw features
that we usually found in the image and audio domains, the input features in web
space are always of multi-field and are mostly discrete and categorical while
their dependencies are little known. Major user response prediction models have
to either limit themselves to linear models or require manually building up
high-order combination features. The former loses the ability of exploring
feature interactions, while the latter results in a heavy computation in the
large feature space. To tackle the issue, we propose two novel models using
deep neural networks (DNNs) to automatically learn effective patterns from
categorical feature interactions and make predictions of users' ad clicks. To
get our DNNs efficiently work, we propose to leverage three feature
transformation methods, i.e., factorisation machines (FMs), restricted
Boltzmann machines (RBMs) and denoising auto-encoders (DAEs). This paper
presents the structure of our models and their efficient training algorithms.
The large-scale experiments with real-world data demonstrate that our methods
work better than major state-of-the-art models.
