Dynamic Sampling for Talent Pool Attribute Estimation
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Solution Overview
Problem
Large online systems with millions of members face challenges in estimating attributes like salary efficiently, as existing methods require processing a large number of samples, leading to delays in real-time interactions and resource inefficiency.
Innovation Solution
Implementing dynamic sampling based on talent pool size, where the number of samples per attribute is adjusted according to the talent pool size, reducing the number of samples needed while maintaining accuracy, by taking 1 sample for pools over 100,000, 10 samples for pools between 1,000 and 100,000, and 100 samples for pools under 1,000, to optimize resource usage and response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of samples are taken from probability distributions to estimate attributes, then measurement precision is improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent applies dynamic sampling by adjusting the number of samples based on talent pool size. For talent pools with more than 100,000 members, only 1 sample is taken per attribute; for pools with 1,000-100,000 members, 10 samples are taken; and for pools with fewer than 1,000 members, 100 samples are taken. This dynamic adjustment resolves the contradiction by adapting sample size to the specific context, maintaining adequate estimation accuracy while significantly improving processing speed for large talent pools.
2Measurement precision
If a large number of samples are processed, then measurement precision is improved, but resource consumption increases
Solution Approach 1:
The system dynamically adjusts computational resource usage by varying sample size according to talent pool size. This prevents unnecessary processing of large numbers of samples for large talent pools where high precision is less critical, while maintaining adequate resource investment for smaller talent pools where estimation accuracy is more important. This resolves the contradiction between measurement precision and resource consumption.
3Measurement precision
If fixed large sample sizes are used for all talent pools, then measurement precision is maintained, but device complexity increases and ease of operation decreases
Solution Approach 1:
The patent changes the parameter of sample size from a fixed value to a variable that depends on talent pool size. This parameter change simplifies the system by establishing clear thresholds (100,000 and 1,000 members) that automatically determine sample size, eliminating the need for complex manual configuration while maintaining adequate estimation accuracy across different talent pool sizes.
Data Source
AI summary
Apparatuses, computer readable medium, and methods are disclosed for dynamic sampling based on talent pool size. The method of dynamic sampling based on talent pool size may include determining, by at least one hardware processor, a talent pool based on a query from a user, where the talent pool comprises members and the members comprise attributes. The method further includes determining based on a size of the talent pool a sampling size number, where the attributes are represented by probability distribution functions. The method may further include determining an aggregate distribution of the attributes by combining the sampling size number of samples from each of the attributes and determining a range of a value of the aggregate distribution of the attributes. The method may further include causing the range of the value of the aggregate distribution of the attributes to be displayed to the user.