Universal Recommendation Template for Multi-Industry Data Processing
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Solution Overview
Problem
In recommendation systems, the need to re-describe interactive data languages for new industries leads to low data processing efficiency, as existing technologies require rebuilding data structures for each industry, resulting in complexity and reduced processing efficiency as more industries are added.
Innovation Solution
A method and apparatus that utilize a universal recommendation template applicable to multiple industries, allowing data from new industries to be processed by matching fields and values with the template, eliminating the need for rebuilding data structures and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a new industry data structure is created for each industry, then data processing accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The patent applies universality by creating a unified data processing framework that can handle multiple industry types (e.g., e-commerce, content, local life services) through a single standardized interface. The system defines universal data structures with standard fields that can accommodate different industry-specific data, eliminating the need to create separate data structures for each industry while maintaining processing accuracy.
Solution Approach 2:
The patent segments the data processing system into modular components: a standardized data receiving module that accepts various industry data formats, a processing module that applies uniform processing logic, and an output module that delivers results. This segmentation allows the system to maintain simplicity while handling diverse industry requirements through standardized interfaces.
2Measurement precision
If industry-specific data structures are rebuilt for new industries, then data representation accuracy is improved, but data processing efficiency deteriorates
Solution Approach 1:
The patent implements a universal data processing framework that can efficiently handle data from multiple industries without requiring structure regeneration. The standardized data structures with predefined fields enable rapid processing while accurately representing industry-specific information through standardized schemas, thus improving both efficiency and representation accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-defining standardized data structures and processing frameworks that can accommodate multiple industries. Instead of creating data structures when needed, the system prepares universal templates in advance that can be directly applied to new industries, eliminating the time-consuming process of structure rebuilding while maintaining accurate data representation.
3Adaptability or versatility
If data structures are regenerated for each new industry, then adaptability to new industries is improved, but loss of time increases
Solution Approach 1:
The patent achieves high industry adaptability through universal data structures that can accommodate various industry types without regeneration. The standardized framework with flexible field definitions allows the system to adapt to new industries (e-commerce, content, local services, etc.) immediately upon data arrival, eliminating time-consuming structure creation while maintaining versatility.
Solution Approach 2:
The patent uses copying by replicating the standardized data processing framework across different industry applications. Instead of creating unique structures for each industry, the system copies and applies the same proven template, which can be efficiently instantiated and configured for new industries without regenerating the core structure, thus reducing time loss while maintaining adaptability.
Data Source
AI summary
The present disclosure relates to the field of computer technology, and discloses a method and apparatus for processing data in a recommendation system, a method and apparatus for recommendation, a device, and a medium. The method provided in the present disclosure includes: obtaining data-to-be-processed of a target industry; analyzing the data-to-be-processed to obtain a plurality of fields and values of the fields in the data-to-be-processed; and querying a recommendation template for a target field corresponding to each field of the fields, and matching a value of the field with the queried target field, to obtain target data for recommendation, wherein the recommendation template is applicable to data-to-be-processed corresponding to a plurality of industries.