Sampling-Based Semantic Vector Generation for User Relationship Networks
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Solution Overview
Problem
Existing methods for generating user relationship networks struggle to effectively represent long-term semantic information of users while reducing the computational burden of text vectorization, leading to incomplete user distinction in the network.
Innovation Solution
A method involving the acquisition of historical text data for users within a preset duration, sampling of this data to obtain representative text data, and generation of semantic vectors using models like ERNIE to create a semantic relationship network that reflects user interactions and behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text vectorization is performed on all historical text data to represent user semantic information, then user distinction and network representation accuracy are improved, but computational burden and processing time increase significantly
Solution Approach 1:
The patent extracts only the most representative text data from the complete historical text corpus through sampling techniques. By identifying and processing only the essential samples that capture user semantic characteristics, the system achieves accurate user distinction without requiring computationally intensive processing of all historical data, thus resolving the contradiction between measurement precision and computational burden.
Solution Approach 2:
The patent applies partial action by processing only a subset of historical text data through sampling rather than complete processing. This selective approach processes just enough data to achieve accurate user representation while avoiding the excessive computational cost of processing the entire historical text corpus, balancing precision requirements with computational efficiency.
2Productivity
If sampling is performed on historical text data to reduce computational load, then processing efficiency is improved, but completeness of user semantic information may be lost
Solution Approach 1:
The patent employs feedback mechanisms in the sampling process to continuously evaluate and adjust the sampling strategy. By monitoring the representativeness of sampled data against the complete historical text distribution, the system optimizes the sampling process to retain sufficient semantic information while maximizing processing efficiency, preventing information loss.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and analyzing historical text data to identify characteristic patterns and semantic structures before actual sampling occurs. This preliminary analysis enables the design of more effective sampling strategies that preserve semantic completeness while reducing the amount of data needing processing, thus maintaining information integrity.
Data Source
AI summary
A relationship network generation method and device, electronic apparatus, and a storage medium are provided, which are related to big data processing. In an implementation, at least one historical text data corresponding to N users within a preset duration is acquired, where N is an integer greater than or equal to 1; sampling is performed on at least one historical text data corresponding to the N users to obtain the sampled text data respectively corresponding to the N users; semantic vectors corresponding to the N users respectively are determined based on the sampled text data corresponding to the N users respectively, and a semantic relationship network involving the N users is generated based on the semantic vectors corresponding to the N users respectively.