A Method for Analyzing Data Services

Abstract

The invention provides a method for analyzing a data service. The method for analyzing the data service includes the following steps: building a correlation model and a type preference model based on using conditions of a user to the data service, and building a feature matching model; then building a data service relation model by making use of the correlation model and the type preference model, and building a data service integrated analysis model by using the data service relation model and the feature matching model; and finally analyzing the data service by means of the data service integrated analysis model and using the analysis result in data service recommendation. By means of the method for analyzing the data service, the accuracy of the analysis result is improved, the analysis result is used in the data service recommendation, and thus the precision of data service recommendation can be improved.

Description

technical field [0001] The invention relates to the technical field of data service support, in particular to a method for analyzing data services. Background technique [0002] At present, data service recommendation is mainly based on products, and potential target user groups are found through data mining technology. Starting from this method, each model is independent and fragmented. At the same time, because each product is recommended separately, it consumes more recommendation resources and frequently disturbs the user, making the user perception weak. [0003] The existing mainstream user preference mining methods mainly include association models, type preference models, and feature matching models. However, each model has limitations in applicability. [0004] The association model has a good prediction effect for mature businesses, and is suitable for predicting the association relationship between businesses that have matured and whose customer groups have alrea...

AI Technical Summary

Problems solved by technology

[0005] The type preference model excavates the logical relationship between businesses and realizes the combination of quantitative and qualitative analysis. The prediction effect of the business in the introduction period is worse than that of the association model, but for the mature business, the prediction result of the type preference model is not accurate enough, and the model prediction The lift metric is not as good as the association model

[0006] The feature matching model digs deep into the relationship between business and customer attributes, consumption and call behavior, and new business behavior. For some businesses, high-precision predictions can be obtained, but for some businesses, the coverage of potential customers is insufficient.

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