Click stream estimation method of neural network

Abstract

A click stream estimation method of a neural network comprises the following steps: 1) collecting a large amount of historical behavior data of a user, the data including possible features helpful toclick stream estimation, such as advertisement commodity information, user information, context information and store information, and constructing a data set on the basis of the features; 2) constructing a weight matrix decomposition cross neural network model of the associated features, wherein the model comprises a logistic block, a word embedding block, a word embedding vector weight cross block and a hidden layer block; and 3) segmenting the data set into small blocks, sequentially inputting the small blocks into the weight matrix decomposition cross neural network of the associated features, and updating parameters by using a back propagation Adam algorithm until the parameters meet early stop condition convergence; and 4) completing click prediction of the advertisement commodity bythe user in an actual system. Relatively high-dimensional data is mapped to a low-dimensional word vector through a word embedding technology, so that the calculation amount is reduced, and learningof a neural network is facilitated.

Description

technical field [0001] The invention relates to user behavior prediction and analysis in the field of computational advertising, in particular to a large-scale data prediction and classification method based on correlation feature weight matrix decomposition cross neural network, which belongs to the field of click stream prediction. Background technique [0002] Click-through rate (CTR) estimation is one of the most challenging and valuable techniques in the field of advertising. Its purpose is to predict the likelihood of an ad being clicked based on historical data. [0003] Online advertising is the primary source of revenue for most Internet companies. In the advertising system, the ranking of an advertisement is determined by the bid and click-through rate of the advertisement. Properly estimating streams is critical and determines the revenue of most Internet companies. We need to predict the possibility of a user clicking on a certain advertised product item (such...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problems of low accuracy and the need for a large number of artificial feature engineering existing in the existing click stream estimation technology, the present invention provides a neural network click stream estimation method

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