Machine Learning Model for Crowdsourced User Productivity Prediction
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Solution Overview
Problem
In crowdsourcing platforms, determining the productivity rate of a user for new types of projects is challenging, as existing methods struggle to account for the 'learning effect' where a user's efficiency changes as they master new tasks, especially when the user has not previously encountered similar tasks.
Innovation Solution
A computer-implemented method and system that uses a machine learning algorithm trained on project-specific and user-task specific features, along with user activity history, to infer the productivity rate for new projects by analyzing the user's learning parameter and efficiency changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine productivity rate for new project types, then the system maintains simplicity, but the accuracy of productivity prediction deteriorates due to inability to account for learning effect
Solution Approach 1:
The system performs preliminary training of machine learning models using historical user activity data before actual productivity prediction is needed. This pre-training phase captures learning patterns and efficiency changes, enabling accurate predictions for new project types without complex real-time calculations.
Solution Approach 2:
The patent introduces machine learning algorithms as intermediaries between raw user activity data and productivity predictions. These algorithms process and interpret complex user behavior patterns, transforming them into meaningful productivity estimates that account for learning effects without requiring direct complex modeling.
2Measurement precision
If the system collects and processes extensive user activity history and interaction features, then the productivity prediction accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system pre-processes and stores user activity history and interaction features in structured formats during the training phase. This preliminary organization of data enables rapid retrieval and processing during actual productivity predictions, reducing computational overhead and processing time.
Solution Approach 2:
The patent extracts and stores only the most relevant features from extensive user activity data, such as completion times, accuracy metrics, and interaction patterns. This selective extraction reduces the volume of data that needs to be processed in real-time while maintaining prediction accuracy.
3Adaptability or versatility
If the system uses machine learning algorithms trained on comprehensive training data, then the ability to predict productivity for unseen task types improves, but the training time and computational resources required increase
Solution Approach 1:
The training process is segmented into multiple phases: initial model training on historical data, fine-tuning on specific project types, and validation. This segmentation allows the system to build general learning patterns first, then adapt to specific domains, reducing overall training time while maintaining versatility.
Solution Approach 2:
The system performs preliminary training on diverse historical user activity data to establish baseline learning patterns. This pre-training creates a robust foundation that can be quickly adapted to new project types with minimal additional training, reducing the time required to handle unseen task types.
Data Source
AI summary
There is disclosed a method and system for determining a productivity rate of a user in a computer-implemented crowd-sourced environment. The method comprises, in a training phase: acquiring a training project; determining, set of project-specific features indicative of one or more characteristics of the training project; acquiring, a plurality of training results; determining a set of user-task specific features; determining, a user activity history associated with the user; generating a set of training data including the set of project-specific features, the set of user-task specific features, and the user activity history; training a machine learning algorithm (MLA), the training including: determining, a set of features representative of a property of the set of training data; and generating an inferred function based on the set of features, the inferred function being configured to determine the productivity rate of the user for a given project.