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The CTR (Click-Through Rate) prediction plays a central role in the domain ofcomputational advertising and recommender systems. There exists several kindsof 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 approachesgenerally use the vector-product of each pair of features, which have ignoredthe different semantic spaces of the feature interactions. In this paper, wepropose a novel Tensor-based Feature interaction Network (TFNet) model, whichintroduces an operating tensor to elaborate feature interactions viamulti-slice matrices in multiple semantic spaces. Extensive offline and onlineexperiments show that TFNet: 1) outperforms the competitive compared methods onthe typical Criteo and Avazu datasets; 2) achieves large improvement of revenueand click rate in online A/B tests in the largest Chinese App recommendersystem, Tencent MyApp.

TFNet: Multi-Semantic Feature Interaction for CTR Prediction