User behavior prediction method, device, equipment and medium

Abstract

The present invention relates to the field of artificial intelligence, and provides a user behavior prediction method, device, equipment, and medium, which can initialize the weights of source domain data sets and target domain data sets to obtain initial weights, use a logistic regression classifier as a base model, and use a single As the final classifier, the model can retain the interpretability of the linear model and be more suitable for actual business. Based on the method of transfer learning, the weight of the source domain dataset and the target domain dataset can be adjusted iteratively to make the training data more accurate. It is applicable to actual application scenarios, and then trained to obtain the optimal model for prediction, which not only solves the problem of lack of samples, but also improves the effect of model training, thereby achieving more accurate user behavior prediction. In addition, the present invention also relates to block chain technology, and the target classifier can be stored in block chain nodes.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a user behavior prediction method, device, equipment and medium. Background technique [0002] Prediction of customer purchase behavior can guide companies to carry out precise marketing. For example, in scenarios such as life insurance, when there are new life insurance products, product sales in new regions, or project restarts, there will be a lack of samples or old samples. At this time, if It is necessary to predict the future purchase behavior of customers to provide marketing support, and it will face the problem of building a business model from 0 to 1. However, the establishment of the model requires a certain amount of data accumulation, and the lack of data is a major obstacle. [0003] In view of the above situation, the usual solution is to construct some strategies based on experience, or wait for the accumulation of data, or directly use the...

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Problems solved by technology

[0004] However, the above method will affect the timeliness and accuracy of the business strategy. For example, when using the method of waiting for data accumulation, the timeliness will be affected because the data accumulation takes a certain amount of time; when directly using the When the existing model predicts the customer's purchase behavior, the accuracy of the prediction will be affected due to the differences in the data between different scenarios.

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