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

Hyphenated HPLC-MS technique for analysis of compositional monosaccharides of transgenic corn glycoprotein and characterization of degradation products of diazinon, fonofos and aldicarb in various oxidation systems

The studies of this dissertation are composed of two sections. The first one deals with the analysis of compositional monosaccharides of transgenic corn glycoproteins. The method used in this study involved derivatization of monosaccharides with two fluorophores followed by HPLC/fluorescence detection for quantitative studies, and by HPLC/SSI/MS for identification confirmation of individual monosaccharide...The second section investigates the degradation processes of several pesticides including diazinon, fonofos and aldicarb in various oxidation systems --Abstract, page iv