Click rate prediction method based on attention mechanism

Abstract

The invention provides a click rate prediction method based on an attention mechanism. The method comprises the following steps: 1, preprocessing the features of a user, and carrying out One-hot one-hot coding on the features of the same type of users to obtain a high-dimensional sparse feature vector; 2, performing dimension reduction on the high-dimensional sparse feature vector through an embedded vector, and taking the feature vector after dimension reduction as an input vector of a click rate model to be respectively substituted into a compression interaction network and a deep neural network; and 3, performing Hadamard product on the input initial feature vector and the input vector of each hidden layer, taking the obtained result as the input value of the next hidden layer, and enabling the combination of the features to rise by one dimension every other hidden layer. The low-dimensional features, the explicit high-dimensional features and the implicit high-dimensional featuresof the user are comprehensively considered, useful feature combinations are screened through a self-attention mechanism, the prediction efficiency is improved, manual feature extraction is not needed,and the high-dimensional feature combinations can be extracted.

Description

technical field [0001] The invention relates to the technical field of Internet applications, in particular to a click rate prediction method based on an attention mechanism. Background technique [0002] With the explosive growth of Internet information, the field of computer science, especially artificial intelligence technology, has also made great progress. As a branch of computer science and applied science, it mainly studies how to use machines to simulate, extend and expand the thinking processes of the human brain (such as memory, learning, reasoning and decision-making). At present, artificial intelligence technology has been successfully applied in many fields such as automatic driving, medical diagnosis, language recognition, image recognition, and financial big data. [0003] Although the industry currently has more in-depth research on click-through rate estimation, there are still some problems in these models, such as large amount of data and sparse data. At ...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a click-through rate prediction method based on the attention mechanism, and its purpose is to solve the problem that the traditional model needs to manually extract features, cannot extract high-dimensional feature combinations, and easily leads to excessively high dimensions

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