xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions
Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep ne...
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Zusammenfassung: | Learning feature interactions is the key to success for the large-scale CTR
prediction and recommendation. In practice, handcrafted feature engineering
usually requires exhaustive searching. In order to reduce the high cost of
human efforts in feature engineering, researchers propose several deep neural
networks (DNN)-based approaches to learn the feature interactions in an
end-to-end fashion. However, existing methods either do not learn both
vector-wise interactions and bit-wise interactions simultaneously, or fail to
combine them in a controllable manner. In this paper, we propose a new model,
xDeepInt, based on a novel network architecture called polynomial interaction
network (PIN) which learns higher-order vector-wise interactions recursively.
By integrating subspace-crossing mechanism, we enable xDeepInt to balance the
mixture of vector-wise and bit-wise feature interactions at a bounded order.
Based on the network architecture, we customize a combined optimization
strategy to conduct feature selection and interaction selection. We implement
the proposed model and evaluate the model performance on three real-world
datasets. Our experiment results demonstrate the efficacy and effectiveness of
xDeepInt over state-of-the-art models. We open-source the TensorFlow
implementation of xDeepInt: https://github.com/yanyachen/xDeepInt. |
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DOI: | 10.48550/arxiv.2301.01089 |