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<p>Click-through rate (CTR) prediction, which estimates the probability that a user will click on an advertisement or an item, is critical to online advertising recommender systems. A key factor in optimizing CTR is understanding how to discover and explain uncommon or hidden feature interactions concealed behind user behaviors. In this paper, a neural additive feature interaction network model is firstly constructed (abbreviated as NAFI), which can automatically learn the low- and high- order feature interactions of input features with good explainability. Then a multi-teacher knowledge distillation network is utilized to realize the lightweight of NAFI (abbreviated as KD-NAFI). Finally, comprehensive experiments on three public datasets demonstrate the accuracy and interpretability of our models.</p>

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Accurate and interpretable CTR prediction via distilled neural additive feature interaction network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Fei Guan,
  • Jiahuan Zhan,
  • Jing Yang

摘要

Click-through rate (CTR) prediction, which estimates the probability that a user will click on an advertisement or an item, is critical to online advertising recommender systems. A key factor in optimizing CTR is understanding how to discover and explain uncommon or hidden feature interactions concealed behind user behaviors. In this paper, a neural additive feature interaction network model is firstly constructed (abbreviated as NAFI), which can automatically learn the low- and high- order feature interactions of input features with good explainability. Then a multi-teacher knowledge distillation network is utilized to realize the lightweight of NAFI (abbreviated as KD-NAFI). Finally, comprehensive experiments on three public datasets demonstrate the accuracy and interpretability of our models.