Latency Sensitivity Inference Using Biased and Unbiased Distributions
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Solution Overview
Problem
Current methods for analyzing the impact of latency on user activities in online services rely on active intervention, which can disrupt user experience and are not effective in identifying true user preferences.
Innovation Solution
A passive approach that infers latency sensitivity by comparing biased and unbiased latency distributions derived from natural user interactions, using machine learning to normalize latency preferences and adjust for confounding factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active intervention methods are used to analyze latency impact, then latency sensitivity can be measured, but user experience is disrupted and true user preferences cannot be identified
Solution Approach 1:
The system uses users' own natural interaction data to infer latency sensitivity without external intervention. By analyzing the biased distribution of latency from actual user actions and comparing it with an inferred unbiased distribution, the system extracts latency preferences from self-generated data, eliminating the need for disruptive active intervention while maintaining measurement precision
2Object-affected harmful factors
If natural user interactions are analyzed, then user experience remains undisturbed, but confounding factors affect the accuracy of latency preference identification
Solution Approach 1:
The system segments the latency distribution into two components: a biased distribution from actual user actions and an inferred unbiased distribution representing the baseline. By separating these components and computing their ratio, the system isolates the latency preference signal from confounding factors such as user activity patterns and temporal variations, thereby maintaining measurement precision while using undisturbed natural interactions
Solution Approach 2:
The system introduces an intermediary inference mechanism that uses the biased distribution as a proxy to estimate the unbiased distribution. This intermediary approach allows the system to account for confounding factors indirectly by modeling their effect on the biased distribution, thereby recovering the true latency preference without direct intervention in user behavior
Data Source
AI summary
The systems and methods may analyze an impact of latency on user activities by leveraging the variation of latency seen in the normal course of user activities with an application. The systems and methods may infer the latency sensitivity of users by comparing a biased latency distribution of user actions to an estimate of the underlying unbiased latency distribution. The systems and methods may compute a normalized latency preference of the users using a biased latency probability distribution function and an unbiased latency probability distribution function. The systems and method may use the normalized latency preference to analyze an impact of latency on the user activities.