FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factorization machine based models and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function. We conduct extensive experiments on two real-world datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).Comment: 8 pages,5 figure

Thawing k-essence dark energy in the PAge space

A broad class of dark energy models can be written in the form of k-essence, whose Lagrangian density is a two-variable function of a scalar field $\phi$ and its kinetic energy $X\equiv \frac{1}{2}\partial^\mu\phi \partial_\mu\phi$. In the thawing scenario, the scalar field becomes dynamic only when the Hubble friction drops below its mass scale in the late universe. Thawing k-essence dark energy models can be randomly sampled by generating the Taylor expansion coefficients of its Lagrangian density from random matrices \cite{thaws}. Ref. \cite{thaws} points out that the non-uniform distribution of effective equation of state parameters $(w_0, w_a)$ of thawing k-essence model can be used to improve the statistics of model selection. The present work studies the statistics of thawing k-essence in a more general framework that is Parameterized by the Age of the universe (PAge) \cite{PAge}. For fixed matter fraction $\Omega_m$, the random thawing k-essence models cluster in a narrow band in the PAge parameter space, providing a strong theoretical prior. We simulate cosmic shear power spectrum data for the Chinese Space Station Telescope optical survey, and compare the fisher forecast with and without the theoretical prior of thawing k-essence. For an optimal tomography binning scheme, the theoretical prior improves the figure of merit in PAge space by a factor of $3.3$.Comment: 24 pages, 5 figures, submission invited by Commun. Theor. Phy