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 rst mapped into low dimensional embedding vectors, and then transformed into xed-length vectors in a group-wise manner, nally 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 xed-length representation vector, in regardless of what candidate ads are. The use of xed-length vector will be a bottleneck, which brings di culty for Embedding&MLP methods to capture user's diverse interests e ectively 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 di erent 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 e ectiveness of proposed approaches, which achieve superior performance compared * Correspondence author is Guorui Zhou. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci c permission and/or a fee. Request permissions from permissions@acm.org. KDD '18, August 19-23, 2018 with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main tra c.
Authors in REF proposed a novel model: Deep Interest Network (DIN) to capture users' diverse interests effectively from their behaviors.
Deep Interest Network for Click-Through Rate Prediction
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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