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Intelligent finance is a new form of business with deep integration of artificial intelligence technology and financial industry. An important application of intelligent finance is the precise marketing of financial products. As a key link in precision marketing, click through rate(CTR) prediction has made great progress, but there is still room for improvement in multiple features fusion, feature interactions learning and other aspects. In view of these needs and challenges, we propose a CTR prediction model named MCGM, which is used to realize precision marketing of financial products. The main characteristics of the model are as follows: (i) in order to effectively fuse multiple features, we design a hierarchical gated mechanism to select salient feature information at different levels; (ii) in order to fully learn the nonlinear relationship between features, we design a multi-channel feature interactions learning module. Specifically, it adopts factorization machine(FM), improved CrossNet(ICN) and multilayer perceptron(MLP) components to model the feature interactions from high-order to low-order, in order to obtain the abstract features containing rich information. Comprehensive and sufficient experiments on real world datasets show that the proposed model achieves better prediction performance compared with baselines. The proposed model not only has specific application value in the field of financial products marketing, but also provides an idea reference for data-driven marketing modeling. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and the increasing demand for legal intelligence, using AI methods to predict legal judgments has become a hot spot in recent years. Charge prediction is one of the core tasks of Legal Judgment Prediction (LJP). It aims to predict charge from complicated legal facts, so as to help the court make judgments or provide legal professional guidance to non-professionals. In the field of legalAI, interpretability is crucial compared to others. Reasonable interpretability can eliminate hidden dangers such as gender discrimination and provide support for judges' decisions. However, how to add the legal theory framework to the modeling to improve the interpretability is a challenge, which has few researches at present. To address this problem, we use Double-layer Criminal System as a guide to build Charge Prediction modeling called DCSCP which aims to predict charges in the criminal law of China. In general, our characteristic is to achieve multi-granularity inference of legal charges by obtaining the subjective and objective elements from the fact descriptions of legal cases. Specifically, our approach is performed in two steps: (1) extract the objective elements from the fact description and use them to generate candidate charges to achieve coarse-grained prediction; (2) extract the subjective elements from the fact description, and design the first-order predicate logic inference to realize the fine-grained charge inference in combination with the candidate charges. Experimental results show that our DCSCP can provide interpretable predictions, and it can maintain performance compared to other state-of-the-art charge prediction models. [ABSTRACT FROM AUTHOR]