Neural Network Resource Fulfilment Prediction Using Time-Based Features
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Solution Overview
Problem
Existing recruitment systems fail to accurately predict the likelihood of resource fulfilment within required timeframes and provide insights into request status, leading to inefficiencies and wasted time in mass recruitments.
Innovation Solution
A multi-layer neural network system that utilizes time-based features to predict resource fulfilment by extracting relevant features, removing redundancies, and training the network for accurate predictions, including determining the chance of closure and timeliness of resource requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing recruitment systems use resume scanning and matching techniques, then candidate suitability ranking is provided, but prediction of resource fulfilment likelihood and timing is not provided
Solution Approach 1:
A neural network model is introduced as an intermediary component between the resume matching system and the resource fulfilment prediction. This intermediary processes historical data and current job requests to generate predictions about fulfilment likelihood and timing, thereby providing the missing information without requiring complete system redesign
Solution Approach 2:
The system performs preliminary analysis by training the neural network on historical recruitment data before making predictions. This preliminary training phase enables the system to learn patterns and relationships that predict resource fulfilment outcomes, allowing predictions to be made before actual recruitment decisions are finalized
2Productivity
If recruitment firms focus on fulfilling more positions, then revenue increases, but ability to predict fulfilment timing and manage expectations deteriorates
Solution Approach 1:
The neural network model provides feedback predictions about fulfilment likelihood and estimated timing for each job request. This feedback mechanism enables recruitment firms to adjust their strategies, set appropriate expectations with clients, and prioritize requests based on predicted outcomes, thereby maintaining prediction accuracy even as fulfilment volume increases
Solution Approach 2:
The system dynamically adjusts predictions based on learned patterns from historical data. The neural network model adapts to changing recruitment conditions and patterns, allowing the system to maintain accurate timing predictions while handling increasing volumes of position fulfilments
3Loss of time
If manual resource management is used, then flexibility is maintained, but time consumption and inefficiency increase
Solution Approach 1:
The neural network model performs automatic predictions of resource fulfilment likelihood and timing without requiring manual intervention. The system self-services by processing job requests and historical data autonomously, generating predictions that reduce the time recruiters spend on manual analysis while maintaining operational simplicity through user-friendly interfaces
Data Source
AI summary
This disclosure relates generally to resource fulfilment prediction and more particularly to training a multi-layer neural network model for resource fulfilment prediction. The conventional resource fulfilment prediction systems typically rely on availability of the requisite skills in the resource requirement request. However, the disclosed system primarily utilizes the time-based features during the modeling process to predict the resource fulfilment accurately. In an embodiment, the system extracts features from a training data including historical resource fulfilment data. The system performs correlation analysis on the extracted features to identify relevant features. The system further derives features using the identified relevant features and uses the derived features in conjunction with the relevant features to train the neural network for resource fulfilment prediction.