Multi-task Learning Framework for Context-Aware User Attribute Estimation
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Solution Overview
Problem
Existing machine learning methods fail to accurately estimate user attribute levels across different contexts in online networks, as they either rely on noisy input graphs or focus on single contexts, lacking the ability to account for varying values and meanings of attributes depending on the context in which they are collected and used.
Innovation Solution
A multi-task learning framework with uncertainty weighting is introduced, incorporating signals from multiple contexts to infer user attribute levels, utilizing multi-task deep learning to estimate attribute values holistically across different contexts, ensuring consistent estimates while capturing task-specific features, and reducing the number of parameters needed compared to single-task models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-task machine learning models are used to estimate user attribute levels, then the model structure is simple, but the estimation accuracy deteriorates when attributes are used across different contexts
Solution Approach 1:
The patent applies multi-task learning framework where a single model structure performs multiple functions by estimating user attribute levels across different contexts simultaneously. The model takes both user features and context features as input, producing context-specific attribute estimates through shared parameters, thereby achieving both structural efficiency and contextual accuracy.
2Measurement precision
If multiple single-task models are used to estimate attribute levels for different contexts, then the estimation accuracy for each context is optimized, but the number of parameters and system complexity increases
Solution Approach 1:
The patent merges multiple single-task models into a single multi-task learning framework. Instead of maintaining separate models for each context, the system combines them into one unified model that shares parameters across tasks. This is achieved through a common neural network architecture that processes both user and context features, reducing the total number of parameters while maintaining context-specific estimation capabilities through task-specific output layers.
3Quantity of substance
If ground-truth data is collected across all contexts, then the training data is comprehensive, but the verification difficulty and data collection cost increase
Solution Approach 1:
The patent applies local quality by collecting and verifying ground-truth data selectively for each context rather than attempting to verify all data uniformly across all contexts. The system identifies which contexts have reliable verification mechanisms and focuses ground-truth collection on those contexts, while using alternative approaches (such as user self-assessments or inferred data) for contexts where verification is difficult or costly.
Data Source
AI summary
In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.