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

• URL: http://arxiv.org/abs/2001.03025v1
• Date: Wed, 8 Jan 2020 10:33:23 GMT
• Title: Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution
• Authors: Shu-Ting Shi, Wenhao Zheng, Jun Tang, Qing-Guo Chen, Yao Hu, Jianke Zhu, Ming Li
• Abstract summary: We argue that it is necessary to consider the continuous-time information in CTR models to track user interest trend from rich historical behaviors. 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.
• Score: 33.090918958117946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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