Intelligent recommendation method, device, and storage medium

An electronic device, comprising:

• a processor and a memory, the processor being connected to the memory, the memory being configured to store computer programs, and the processor being configured to execute the computer programs stored in the memory to cause the electronic device to execute:
• obtaining historical user data and product data corresponding to a diagnosis products, a being a positive integer;
• constructing a historical user portrait according to b preset usage scenarios and the historical user data, b being a positive integer;
• determining a recommendation rule according to c preset user demands, the product data, and the historical user portrait;
• c being a positive integer;
• classifying the a diagnosis products according to the product data, the historical user portrait, and the recommendation rule, to obtain a classification label sets;
• each diagnosis product corresponding to one classification label set, and each classification label set comprising at least one classification label;
• obtaining interaction content between a user and a preset large language model;
• determining user preference information according to the interaction content;
• determining, according to the user preference information, a target classification label set from the a classification label sets;
• and
• outputting, through the preset large language model, related recommended content of a target diagnosis product corresponding to the target classification label set to the user.

A non-transitory computer-readable storage medium, storing a computer program, wherein the computer program is configured to be executed by a processor to execute:

• obtaining historical user data and product data corresponding to a diagnosis products, a being a positive integer;
• constructing a historical user portrait according to b preset usage scenarios and the historical user data, b being a positive integer;
• determining a recommendation rule according to c preset user demands, the product data, and the historical user portrait;
• c being a positive integer;
• classifying the a diagnosis products according to the product data, the historical user portrait, and the recommendation rule, to obtain a classification label sets;
• each diagnosis product corresponding to one classification label set, and each classification label set comprising at least one classification label;
• obtaining interaction content between a user and a preset large language model;
• determining user preference information according to the interaction content;
• determining, according to the user preference information, a target classification label set from the a classification label sets;
• and
• outputting, through the preset large language model, related recommended content of a target diagnosis product corresponding to the target classification label set to the user.