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

Autor:	Zhou, Chang, Zhao, Yang, Zou, Yuelin, Cao, Jin, Fan, Wenhan, Zhao, Yi, Cheng, Chiyu
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by the 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS 2024), 2024 IEEE
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.10239 Zobrazit plný text záznamu View this record from Arxiv