  Click-through rate (CTR) prediction is a critical task in online advertising
systems. Models like Deep Neural Networks (DNNs) are simple but stateless. They
consider each target ad independently and cannot directly extract useful
information contained in users' historical ad impressions and clicks. In
contrast, models like Recurrent Neural Networks (RNNs) are stateful but
complex. They model temporal dependency between users' sequential behaviors and
can achieve improved prediction performance than DNNs. However, both the
offline training and online prediction process of RNNs are much more complex
and time-consuming. In this paper, we propose Memory Augmented DNN (MA-DNN) for
practical CTR prediction services. In particular, we create two external memory
vectors for each user, memorizing high-level abstractions of what a user
possibly likes and dislikes. The proposed MA-DNN achieves a good compromise
between DNN and RNN. It is as simple as DNN, but has certain ability to exploit
useful information contained in users' historical behaviors as RNN. Both
offline and online experiments demonstrate the effectiveness of MA-DNN for
practical CTR prediction services. Actually, the memory component can be
augmented to other models as well (e.g., the Wide&Deep model).
