arXiv 2001.030252020-01-08cs.LGstat.ML

基于深度时间流的兴趣演化预测模型

Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution

Nanjing University（南京大学）；Alibaba Group（阿里巴巴集团）；Zhejiang University（浙江大学）

Shu-Ting Shi, Wenhao Zheng, Jun Tang, Qing-Guo Chen, Yao Hu, Jianke Zhu, Ming Li

备注：: 8 pages. arXiv admin note: text overlap with arXiv:1809.03672 by other authors
发表信息：: AAAI 2020

摘要：

点击率（CTR）预测是视频推荐等工业应用中的一项重要任务。最近，已经提出了深度学习模型来学习用户的整体兴趣的表示，而忽略了兴趣可能随时间动态变化的事实。我们认为，有必要考虑CTR模型中的连续时间信息，以跟踪用户的兴趣趋势，从丰富的历史行为。在本文中，我们提出了一种新的深度时间流框架（Deep Time-Stream framework，缩写为ESTF），它通过一个常微分方程（odd differential equations，缩写为ODE）来引入时间信息。该方法使用神经网络连续地对兴趣的演变进行建模，从而能够解决基于用户的历史行为动态地表示用户兴趣的挑战。此外，我们的框架可以通过利用额外的时间流模块无缝地应用于任何现有的深度CTR模型，而不对原始CTR模型进行任何更改。在公共数据集和真实工业数据集上的实验结果表明了所提方法的有效性，与现有方法相比，具有更好的性能。

英文摘要：

Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation. Recently, deep learning models have been proposed to learn the representation of users' overall interests, while ignoring the fact that interests may dynamically change over time. We argue that it is necessary to consider the continuous-time information in CTR models to track user interest trend from rich historical behaviors. In this paper, we propose a novel Deep Time-Stream framework (DTS) which introduces the time information by an ordinary differential equations (ODE). DTS continuously models the evolution of interests using a neural network, and thus is able to tackle the challenge of dynamically representing users' interests based on their historical behaviors. In addition, our framework can be seamlessly applied to any existing deep CTR models by leveraging the additional Time-Stream Module, while no changes are made to the original CTR models. Experiments on public dataset as well as real industry dataset with billions of samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with existing methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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