TFNet: Multi-Semantic Feature Interaction for CTR Prediction

@article{Wu2020TFNetMF,
  title={TFNet: Multi-Semantic Feature Interaction for CTR Prediction},
  author={Shu Wu and Feng Yu and Xueli Yu and Q. Liu and Liang Wang and Tieniu Tan and Jie Shao and Fan Huang},
  journal={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2020}
}
  • Shu Wu, Feng Yu, +5 authors Fan Huang
  • Published 29 June 2020
  • Computer Science
  • Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems. There exists several kinds of methods proposed in this field, such as Logistic Regression (LR), Factorization Machines (FM) and deep learning based methods like Wide&Deep, Neural Factorization Machines (NFM) and DeepFM. However, such approaches generally use the vector-product of each pair of features, which have ignored the different semantic spaces of the feature… Expand
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