Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914834916835328 |
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| author | Zhou, Chang Zhao, Yang Zou, Yuelin Cao, Jin Fan, Wenhan Zhao, Yi Cheng, Chiyu |
| author_facet | Zhou, Chang Zhao, Yang Zou, Yuelin Cao, Jin Fan, Wenhan Zhao, Yi Cheng, Chiyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising Zhou, Chang Zhao, Yang Zou, Yuelin Cao, Jin Fan, Wenhan Zhao, Yi Cheng, Chiyu Information Retrieval Machine Learning 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. |
| title | Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2406.10239 |