Financial product recommendation method and device based on user portrait

Abstract

The invention provides a financial product recommendation method and device based on a user portrait, and the method comprises the steps: classifying financial users according to the financial income, so as to generate a first classification result; classifying the financial management users according to demographic features and finance features of the financial management users to generate a second classification result; and in the second classification result, pushing the financial management product corresponding to the first financial management user in the first classification result to a second financial management user. According to the invention, new financial management accurate real-time recommendation logic is constructed based on high-quality customer group attention and user attention. The problems that the traditional financial management recommendation content is only recommended according to the return rate, so that the content is rigid, the market real-time dynamic state and the customer real-time financial management concern are difficult to reflect, and the recommendation is not accurate enough are solved.

Description

technical field [0001] The present application belongs to the technical field of big data, and specifically relates to a method and device for recommending wealth management products based on user portraits. Background technique [0002] In the prior art, the recommended content for wealth management is only recommended based on the rate of return, which cannot reflect the current sentiment of the market, and the recommended content is rigid. On the other hand, the rate of return only proves from the product side that the rate of return of the product is not bad, and cannot reflect the market. Chinese customers are optimistic about the product. The future rate of return of wealth management products is affected by a variety of objective factors. The current rate of return cannot help customers judge the future trend, and the user type is not considered in the recommendation, resulting in an inaccurate recommendation. [0003] To sum up, in the prior art, it is difficult for...

AI Technical Summary

Problems solved by technology

[0002] In the existing technology, the recommendation content for financial management is only based on the rate of return, which cannot reflect the current sentiment of the market, and the recommendation content is rigid. On the other hand, the rate of return only proves that the product rate of return is not bad from the product side, and cannot reflect the market How optimistic are customers about the product

The future rate of return of wealth management products is affected by various objective factors. The current rate of return cannot help customers judge the future trend, and the type of user is not considered in the recommendation, resulting in inaccurate recommendations

[0003] To sum up, in the existing technology, the recommendation of wealth management products is difficult to reflect the real-time dynamics of the market and the real-time financial concerns of customers, resulting in the problem that the recommendation is not accurate enough

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