Event-driven financial product recommendation method, device, medium and equipment
By collecting multimodal data and reconstructing user product interaction graphs, and combining risk preferences, a neural network model is used to recommend financial products, solving the problem of low recommendation accuracy in existing technologies and enabling rapid response and accurate recommendations for emergencies.
Patent Information
- Application Number
- CN202610478974.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing financial product recommendation technologies struggle to capture the complex interactions between users and products, as well as the intricate dynamics of the financial market. They also lack response mechanisms for major events, resulting in low recommendation accuracy.
By collecting multimodal data, using feature extraction and natural language processing models to detect financial events, reconstructing user product interaction graphs, combining user risk preferences to perform product screening and recommendation probability calculations, and employing a multi-layer neural network model for product recommendation.
It improves the response speed of the recommendation system to emergencies, ensures that the recommendation results match the user's risk tolerance, and enhances the accuracy and security of financial product recommendations.
Smart Images
Figure CN122415186A_ABST