Financial product recommendation method, apparatus, computer device and storage medium

Abstract

The invention discloses a financial product recommendation method, a device, a computer device and a storage medium, which improve the recommendation effectiveness of the financial product and the applicability of the overall recommendation. The method includes: acquiring the data of financial products in the financial product set and the historical investment data of users; According to the collaborative recommendation algorithm and the user historical investment data, determining a probability that a second financial product is recommended when a user purchases a first financial product fromthe financial product set, the first financial product and the second financial product being different financial products in the financial product set, and the second financial product being financial products remaining in the financial product set other than the first financial product; Determining a product relationship between the first financial product and the second financial product in the financial product set according to the financial product data; Determining a target financial product from a second financial product based on the probability and the product relationship; The target financial product is recommended to the user.

Description

technical field [0001] The present application relates to the field of intelligent recommendation in data analysis, and in particular to a financial product recommendation method, device, computer equipment and storage medium. Background technique [0002] With the rapid development of smart terminal devices such as mobile phones, there are more and more various application APPs. For example, the One Account APP has a wide range of applications. Sometimes, some recommendation algorithms are often used to recommend relevant content to users through APP, especially the recommendation of financial-related products. The recommendation algorithm recommends to the user products that the user may be interested in. [0003] In the process of research and practice of traditional technology, it is found that although the above-mentioned traditional algorithm has achieved certain results and can recommend content that the user may be interested in, it has the following limitations. Fi...

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Problems solved by technology

[0003] In the process of research and practice of traditional technology, it is found that although the above-mentioned traditional algorithm has achieved certain results and can recommend content that the user may be interested in, it has the following limitations. First, there are many products involved in financial products , such as various insurance products, futures products, wealth management products, etc., the cost gap of various financial products is also relatively large, so the method of using the user's interest as a reference point is relatively simple. Therefore, the effectiveness and applicability of such recommendation algorithms are limited to a certain extent.

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