Service Customizing Device Using Pre-Trained User Group Label Recognition Model
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Solution Overview
Problem
Current methods for determining user behavior characteristic labels from massive user-generated content are inefficient, limiting the customization of services for financial companies, resulting in low service customization efficiency.
Innovation Solution
A data source-based service customizing device and method that acquires user-generated content from various data sources, recognizes user group labels using a pre-trained user group label recognition model, and determines corresponding group services based on a predetermined mapping relation, enabling efficient service customization across a large range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user behavior characteristic labels are determined for each user from massive user-generated content, then service customization coverage is improved, but service customization efficiency deteriorates
Solution Approach 1:
The patent segments users into user groups based on behavior characteristics derived from user-generated content. Instead of processing each user individually, the system divides the massive user base into manageable segments (user groups) that can be processed collectively, thereby improving efficiency while maintaining customization coverage.
Solution Approach 2:
The patent creates user group labels as representations or copies of individual user behavior characteristics. By working with these aggregated labels rather than individual user profiles, the system achieves service customization at scale without the computational burden of processing each user separately.
2Speed
If user group labels are recognized using a pre-trained model, then recognition speed is improved, but model training complexity increases
Solution Approach 1:
The patent implements pre-training of the user group label recognition model in advance, before actual service customization operations. This preliminary action allows the model to learn from historical data and be ready for rapid inference, separating the complex training phase from the speed-critical recognition phase.
Solution Approach 2:
The system uses the pre-trained model to automatically recognize user group labels without requiring manual intervention or real-time complex processing. The model serves itself by making predictions based on learned patterns, improving recognition speed while containing complexity within the pre-training phase.
Data Source
AI summary
The disclosure relates to a data source-based service customizing device, method and system, and a computer readable storage medium. The data source-based service customizing device includes: a memory, a processor and the data source-based service customizing system stored on the memory and operated on the processor. The data source-based service customizing system is executed by the processor to implement the following steps: acquiring user generated contents in various predetermined data sources; recognizing the user generated contents by using a user group label recognition model generated by pre-training to recognize user group labels corresponding to the various data sources; determining group services corresponding to the various data sources according to a predetermined mapping relation between the user group labels and the group services, and sending the various data sources and the corresponding group services to a predetermined terminal to perform group service customization on the various data sources.