  Click-through rate prediction is an essential task in industrial
applications, such as online advertising. Recently deep learning based models
have been proposed, which follow a similar Embedding\&MLP paradigm. In these
methods large scale sparse input features are first mapped into low dimensional
embedding vectors, and then transformed into fixed-length vectors in a
group-wise manner, finally concatenated together to fed into a multilayer
perceptron (MLP) to learn the nonlinear relations among features. In this way,
user features are compressed into a fixed-length representation vector, in
regardless of what candidate ads are. The use of fixed-length vector will be a
bottleneck, which brings difficulty for Embedding\&MLP methods to capture
user's diverse interests effectively from rich historical behaviors. In this
paper, we propose a novel model: Deep Interest Network (DIN) which tackles this
challenge by designing a local activation unit to adaptively learn the
representation of user interests from historical behaviors with respect to a
certain ad. This representation vector varies over different ads, improving the
expressive ability of model greatly. Besides, we develop two techniques:
mini-batch aware regularization and data adaptive activation function which can
help training industrial deep networks with hundreds of millions of parameters.
Experiments on two public datasets as well as an Alibaba real production
dataset with over 2 billion samples demonstrate the effectiveness of proposed
approaches, which achieve superior performance compared with state-of-the-art
methods. DIN now has been successfully deployed in the online display
advertising system in Alibaba, serving the main traffic.
