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

2024/06/04 by Chang Zhou, Zhou, Chang, Yang Zhao +11 · 1 citation
Business, Management and Accounting · #E-commerce and Technology Innovations #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2406.10239

openalex publication_date 2024/06/04 · openalex created_date 2024/06/19 · openalex updated_date 2026/07/28

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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