  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.
