Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising

• URL: http://arxiv.org/abs/2406.10239v1
• Date: Tue, 4 Jun 2024 05:52:14 GMT
• Title: Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising
• Authors: Chang Zhou, Yang Zhao, Yuelin Zou, Jin Cao, Wenhan Fan, Yi Zhao, Chiyu Cheng,
• Abstract summary: This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model. This research focuses on localized user behavior activation for tailored ad targeting by leveraging extensive user behavior data.
• Score: 36.61520168259678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model, specifically applied to the advertising system of Alibaba's Taobao platform. Unlike traditional deep learning approaches, this research focuses on localized user behavior activation for tailored ad targeting by leveraging extensive user behavior data. Compared to traditional models, this method demonstrates superior ability to handle diverse and dynamic user data, thereby improving the efficiency of ad systems and increasing revenue.

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