Product portfolio recommendation method, device, equipment, medium and program product

By constructing a product portfolio recommendation model based on multi-dimensional features and utilizing the quantum annealing algorithm, the problem of the inability to recommend multiple types of products simultaneously in existing technologies has been solved, achieving efficient and accurate product portfolio recommendations, thereby enhancing merchant competitiveness and user satisfaction.

CN122134416APending Publication Date: 2026-06-02INDUSTRIAL AND COMMERCIAL BANK OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
INDUSTRIAL AND COMMERCIAL BANK OF CHINA
Filing Date
2025-08-13
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing product recommendation methods cannot recommend multiple types of products to target users at once, resulting in low recommendation efficiency and accuracy, and failing to meet the diversified and personalized needs of product recommendations.

Method used

By extracting multi-dimensional features from products and users, a product portfolio recommendation model is constructed and converted into the Hamiltonian of the Ising model. Quantum annealing is then used to quickly converge to the global optimum. The product portfolio recommendation is then performed by combining the objective function and constraints.

Benefits of technology

It significantly improves the efficiency and accuracy of product combination recommendations, enhances merchants' market competitiveness and user satisfaction, and solves the problems of scalability bottlenecks and low recommendation efficiency in traditional recommendation systems.

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Abstract

This disclosure provides a product portfolio recommendation method applicable to big data, artificial intelligence, and fintech technologies. The method includes: extracting multi-dimensional product features from preprocessed product history information and extracting multi-dimensional user features from preprocessed user history information; constructing a product portfolio recommendation model based on a product-user maximization matching objective function and preset constraints, utilizing the multi-dimensional product and user features; converting the product portfolio recommendation model into a Hamiltonian of the Ising model and performing quantum annealing on the Hamiltonian to obtain the product portfolio recommendation result. This disclosure also provides a product portfolio recommendation apparatus, device, storage medium, and program product.
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