Abstract

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.

Keywords

Computer scienceEmbeddingActivation functionArtificial intelligenceBottleneckRepresentation (politics)Regularization (linguistics)Deep learningTask (project management)Multilayer perceptronMachine learningData miningArtificial neural network

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Publication Info

Year
2018
Type
article
Pages
1059-1068
Citations
1902
Access
Closed

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1902
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Cite This

Guorui Zhou, Xiaoqiang Zhu, Chenru Song et al. (2018). Deep Interest Network for Click-Through Rate Prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 1059-1068. https://doi.org/10.1145/3219819.3219823

Identifiers

DOI
10.1145/3219819.3219823
arXiv
1706.06978

Data Quality

Data completeness: 84%