Self-attention Deep Field-embedded Factorization Machine for Click-through Rate Prediction

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- Self-attention Deep Field-embedded Factorization Machine for Click-through Rate Prediction
- “在数字广告领域，专家提出了自注意力深度域嵌入因子分解机模型，通过精化嵌入向量和特征交互，实现点击率预测，性能优于主流模型。”
- Advanced Engineering Sciences Vol. 56, Issue 5, Pages: 287-296(2024)
- 作者机构：
  
  华东交通大学信息与软件工程学院，江西南昌 330013
- 作者简介：
- 基金信息：
- DOI：10.12454/j.jsuese.202201373
  CLC： TP391
- Published：20 September 2024，
  
  Published Online：03 June 2024，
  
  Received：20 December 2022，
  
  Revised：23 August 2023，
- 稿件说明：
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李广丽,叶艺源,许广鑫,等.面向点击率预测的自注意力深度域嵌入因子分解机[J].工程科学与技术,2024,56(5):287–296

Li Guangli,Ye Yiyuan,Xu Guangxin,et al.Self-attention deep field-embedded factorization machine for click-through rate prediction[J].Advanced Engineering Sciences,2024,56(5):287–296
李广丽,叶艺源,许广鑫,等.面向点击率预测的自注意力深度域嵌入因子分解机[J].工程科学与技术,2024,56(5):287–296 DOI： 10.12454/j.jsuese.202201373.

Li Guangli,Ye Yiyuan,Xu Guangxin,et al.Self-attention deep field-embedded factorization machine for click-through rate prediction[J].Advanced Engineering Sciences,2024,56(5):287–296 DOI： 10.12454/j.jsuese.202201373.

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