Financial service platform product personalized recommendation method and system

Abstract

The invention relates to the technical field of big data, and particularly provides a financial service platform product personalized recommendation method. A financial service platform product personalized recommendation method comprises the following steps: S1, creating a user portrait, including the following three parts: S101, collecting data, S102, analyzing the data, and S103, forming a portrait; s2, financial product recommendation including the following two parts: S201, submitting a demand by a user, and S202, a financial product recommendation function. Compared with the prior art, the financial service platform product personalized recommendation method has the advantage that the recommendation of the products is more accurate. For institutions or enterprises with huge data volume, the method can be used for improving the efficiency of a financial service platform, and has a good popularization value.

Description

technical field [0001] The invention relates to the field of big data technology, and specifically provides a method and system for personalized recommendation of financial service platform products. Background technique [0002] With the rapid development of the big data era, many organizations and enterprises need to maintain or analyze an ever-increasing amount of data. The financial service industry needs more accurate and targeted analytical portraits of users. Traditional and backward data usage cannot adapt to the current large amount of data. Most of the data is stored in the database and cannot be effectively used, and it takes time and effort to extract relevant data. At present, most financial service platforms require users to understand the details of products one by one, which means that time is wasted and a lot of energy is spent. In addition, user portraits have certain limitations, such as the lack of some data and low timeliness of data. Contents of the i...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of the big data era, many institutions and enterprises need to maintain or analyze an ever-increasing amount of data. The financial service industry needs more accurate and directional analysis of users. Traditional and backward data usage cannot adapt to the current large data volume.

Most of the data is stored in the database and cannot be effectively used, and it takes time and effort to extract relevant data

At present, most financial service platforms require users to understand the details of the products one by one, which means that time is wasted and a lot of energy is spent; in addition, user portraits have certain limitations, such as the lack of some data and low data timeliness.

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