This article introduces the principles, implementation and application of the Wide & Deep model and DCN (Deep & Cross Network) model in processing feature learning. The article describes in detail the simple linear transformation of the Wide part and the multi-layer perceptron structure of the Deep part, and explains how to improve the Wide part through Cross Network to explicitly model the intersection between features....

This article introduces the FM model, which is a statistical model used to deal with high-dimensional sparse data and feature intersection, and can capture the relationship between features. The FM model reduces the number of computational parameters and increases the capture of feature interactions by introducing low-rank factor vectors, paying special attention to second-order interactions. The model uses gradient descent to optimize the loss function and learns the interaction between users and items through factor vectors. In recommendation systems, FM models can be used to predict users' ratings or click probabilities of items by analyzing the interaction of features between users and items....