A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features

TL;DR

This paper introduces FiiNet, a CTR prediction model that explicitly constructs multi-order feature crosses and dynamically learns their importance, enhancing both prediction accuracy and interpretability.

Contribution

The paper proposes FiiNet, a novel neural network that uses SKNet to explicitly model multi-order feature interactions and assess their importance for improved CTR prediction.

Findings

FiiNet outperforms existing models on real datasets.

The model enhances interpretability of feature interactions.

Dynamic importance learning improves prediction accuracy.

Abstract

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.