Most current click-through rate prediction(CTR)models create explicit or implicit high-order feature crosses through Hadamard product or inner product, with little attention to the importance of feature crossing; only few models are either limited to the second-order explicit feature crossing, implicitly to high-order feature crossing, or can learn the importance of high-order explicit feature crossing but fail to provide good interpretability for the model. This paper proposes a new model, FiiNet (Multiple Order Feature Interaction Importance Neural Networks). The model first uses the selective kernel network (SKNet) to explicitly construct multi-order feature crosses. It dynamically learns the importance of feature interaction combinations in a fine grained manner, increasing the attention weight of important feature cross combinations and reducing the weight of featureless crosses. To verify that the FiiNet model can dynamically learn the importance of feature interaction combinations in a fine-grained manner and improve the model's recommendation performance and interpretability, this paper compares it with many click-through rate prediction models on two real datasets, proving that the FiiNet model incorporating the selective kernel network can effectively improve the recommendation effect and provide better interpretability. FiiNet model implementations are available in PyTorch.

翻译：当前大多数点击率预测模型通过哈达玛积或内积构建显式或隐式的高阶特征交叉，但很少关注特征交叉的重要性；仅有少数模型要么局限于二阶显式特征交叉、隐式高阶特征交叉，要么能够学习高阶显式特征交叉的重要性但缺乏良好的模型可解释性。本文提出一种新模型FiiNet（多阶特征交互重要性神经网络）。该模型首先利用选择性核网络显式构建多阶特征交叉，以细粒度方式动态学习特征交互组合的重要性，增加重要特征交叉组合的注意力权重，降低无效特征交叉的权重。为验证FiiNet模型能够以细粒度方式动态学习特征交互组合的重要性并提升推荐性能与可解释性，本文在两个真实数据集上将其与多种点击率预测模型进行对比，证明融合选择性核网络的FiiNet模型能有效提升推荐效果并提供更好的可解释性。FiiNet模型基于PyTorch实现。