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Financial product content recommendation method and system and computer readable storage medium

A financial product and content recommendation technology, applied in finance, computing, neural learning methods, etc., to achieve high recommendation adoption rate and accurate and complete customer information

Pending Publication Date: 2021-06-29
珠海华发金融科技研究院有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose a financial product content recommendation method, system and computer-readable storage medium in order to solve the technical problem of how to implement content recommendation on the Internet product platform

Method used

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  • Financial product content recommendation method and system and computer readable storage medium
  • Financial product content recommendation method and system and computer readable storage medium
  • Financial product content recommendation method and system and computer readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] This embodiment provides a method for recommending financial product content, which can push product content to users more accurately and precisely; it includes the following steps:

[0053] Step 1: Obtain the user preference data information identified by the mobile phone user identifier from multiple different data sources;

[0054] Step 2: Input the user preference information into the convolutional neural network module and generate a content catalog of financial products to be recommended;

[0055] Step 3: Retrieving financial product information related to the content catalog of financial products to be recommended from the financial product information database according to the content information in the content catalog of financial products to be recommended;

[0056] Step 4: Visually display the retrieved financial product information on the user terminal.

[0057]Preferably, in one of the preferred technical solutions of this embodiment, in the step 1, the pl...

Embodiment 2

[0060] In this embodiment, on the basis of embodiment 1, in the step 2, the content recommendation model is a recommendation calculation unit, including: a deep learning algorithm and a delivery rule.

[0061] Preferably, in one of the preferred technical solutions of this embodiment, in the step 2, the convolutional neural network includes: a content recommendation model and a method recommendation model.

[0062] Preferably, in one of the preferred technical solutions of this embodiment, the recommendation result of the method recommendation model includes: at least one of products, services, and advertising activities.

[0063] Preferably, in one of the preferred technical solutions of this embodiment, if the recommendation result includes multiple recommended contents, the multiple recommended contents are sorted and recommended to corresponding customers according to corresponding recommendation methods.

[0064] It should be noted that the above-mentioned deep learning a...

Embodiment 3

[0075] This embodiment provides a method for recommending financial product content, which can push product content to users more accurately and precisely; it includes the following steps:

[0076] Step 1: Collect user preference data information and user behavior information from multiple different data sources, and generate a user information set based on the user preference data information and user behavior information; the user preference data information includes: clothing, beauty makeup, At least one of sports, technology, fitness, food, finance, lending, real estate, leasing, history and geography; the user behavior information includes: including: click, favorite, share, like, follow, play, like, dislike At least one of like and report;

[0077] Step 2: Construct a content recommendation model based on the user information set, and train a convolutional neural network;

[0078] Step 3: Push recommended content to customers through the convolutional neural network.

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Abstract

The invention relates to the technical field of content recommendation, and particularly discloses a financial product content recommendation method and system and a computer readable storage medium. The method comprises the following operation steps: 1, acquiring user preference data information identified by a mobile phone user identifier from a plurality of different data sources; 2, inputting the user preference information into a convolutional neural network module and generating a financial product content directory; 3, according to the content information in the catalog, calling financial product information related to the catalog content from a financial product information database; and 4, displaying the financial product information on the user side in a visual mode. The financial product content recommendation system comprises a user preference database, a financial product information database, a server side and a user side. The computer readable storage medium stores a computer program, and when the computer program is executed by the processor, the computer program is used for realizing the financial product content recommendation method; and the method is used for solving the technical problem of accurate recommendation of financial product contents.

Description

technical field [0001] The present invention relates to the technical field of Internet product recommendation, in particular to a financial product content recommendation method, system and computer-readable storage medium. Background technique [0002] Although the one-pass unified account used on the existing financial management platform can manage all the accounts registered on the comprehensive financial management platform to meet various financial management needs such as insurance, banking, and investment. However, with the advent of the Internet information age, the number of platforms, the number of users and online marketing data are increasing day by day, and the types of financial products are also constantly developing in diversification. More and more companies have begun to study product recommendation models to recommend products that users are interested in or urgently need to buy. Existing product recommendation methods are mainly based on historical sal...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06Q40/06G06F16/9535G06N3/04G06N3/08
CPCG06Q30/0631G06Q40/06G06F16/9535G06N3/04G06N3/08
Inventor 蒋绪芳雷宁冯智斌庄荣墩孙雨
Owner 珠海华发金融科技研究院有限公司