Automobile financial product recommendation method and system based on artificial intelligence

Through artificial intelligence-based methods, analyzing and screening the candidate product method group in automobile financial product recommendations has been solved, and a more accurate matching of user needs has been achieved.

CN119648344AActive Publication Date: 2025-03-18CHENGDU WANWANG SECONDARY PLANET COMM EQUIP CO LTD
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Patent Information

Application Number
CN202411856065.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-17
Publication Date
2025-03-18
Estimated Expiration
2044-12-17

AI Technical Summary

Technical Problem

The reliability of recommendations for automobile financial products in the prior art is relatively low, and users find it difficult to choose suitable financial products due to insufficient knowledge or experience.

Method used

Using the automotive financial product recommendation method based on artificial intelligence, we use the candidate automotive financial product cluster and financial product recommendation method cluster to determine the candidate product method cluster, and use the pre-trained neural network to analyze the product recommendation success rate and filter out the target product method cluster.

Benefits of technology

It improves the reliability of automotive financial product recommendations and ensures that the recommendation results are more in line with the actual needs and acceptance of users.

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Abstract

The invention provides an automobile financial product recommendation method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, determining a candidate automobile financial product cluster and a candidate financial product recommendation mode cluster; secondly, performing any team forming processing on each candidate automobile financial product in the candidate automobile financial product cluster and each candidate financial product recommendation mode in the candidate financial product recommendation mode cluster to form a plurality of candidate product mode groups; then, analyzing a product recommendation success rate corresponding to each candidate product mode group; and finally, screening out a target product mode group according to the product recommendation success rate, and respectively taking the candidate automobile financial products and the candidate financial product recommendation modes in the target product mode group as a target automobile financial product and a target financial product recommendation mode. Based on the content, the problem that in the prior art, the reliability of automobile financial product recommendation is relatively low can be solved.
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