Product recommendation method, system, device, equipment, medium and program product
By integrating user information, point information and location information, using a load balancer to allocate product business data and calculate product preferences, the problem of difficult to quickly recommend products in massive product data is solved, and efficient and accurate product recommendations are achieved.
CN119722219APending Publication Date: 2025-03-28INDUSTRIAL AND COMMERCIAL BANK OF CHINA
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Patent Information
- Application Number
- CN202411734058.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
It is difficult for the prior art to quickly and accurately analyze recommended products from massive product data, especially in different terminal devices and Internet environments.
Method used
By integrating user information, buried point information and location information, the load balancer is used to allocate product business data to the target server, and the product preference is calculated based on user weights and buried point weights, thereby recommending products to the corresponding areas.
Benefits of technology
It quickly concentrates regional product business data, improves the calculation speed and accuracy of product preferences, and thus improves the accuracy and user experience of product recommendations.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides a product recommendation method, system, device and equipment, a medium and a program product, and can be applied to the technical field of big data, the technical field of artificial intelligence and the field of financial science and technology. The product recommendation method is applied to a server cluster and comprises the steps that product service data from different terminals are sent to a load balancer, the product service data comprise user information, burying point information and position information, the burying point information is generated when a product burying point is triggered, and the product burying point is arranged on a user-operable product interface of the terminal; according to the position information, a load balancer is used to distribute the product business data to a target server, and the target server is determined from a plurality of servers in a server cluster according to the position information; according to the user weight of the user information and the burying point weight of the burying point information, calculating to obtain a product preference degree corresponding to the position information; and recommending a product to the region corresponding to the position information according to the product preference degree.
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