Augmented Neural Network Cold-Start Prediction
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Solution Overview
Problem
Machine learning models face challenges in predicting the occurrence of new events when there is little or no data available, commonly referred to as a 'cold start' scenario, as they rely on behavior information which may not be present for new items or events.
Innovation Solution
An augmented machine learning model is generated by incorporating parameters from a companion machine learning model trained on non-behavior information, such as textual, visual, or metadata associated with new items, allowing predictions without relying on existing behavior data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model relies on behavior information for predictions, then prediction accuracy for existing items is improved, but the model cannot make predictions for new items without behavior data (cold start problem)
Solution Approach 1:
The patent segments the feature space into behavior-based features and non-behavior-based features. The model processes these segmented features through separate pathways before combining them for final predictions, allowing it to handle both cold start and warm start scenarios effectively
Solution Approach 2:
The patent introduces non-behavior information (such as item attributes, descriptions, or metadata) as an intermediary that bridges the gap when behavior information is unavailable. This intermediary enables the model to generate predictions for new items by leveraging alternative data sources
2Measurement precision
If the model uses only behavior information for training, then it performs well on existing items, but it fails to generalize to new items with no behavior history
Solution Approach 1:
The patent creates a universal model that can handle multiple scenarios (cold start and warm start) by incorporating both behavior-based and non-behavior-based features. The model structure allows it to adapt its feature usage based on data availability, making it multi-functional across different prediction scenarios
3Adaptability or versatility
If the model is trained on non-behavior information only, then it can predict new items, but it loses the ability to accurately predict based on user behavior patterns
Solution Approach 1:
The patent merges behavior-based features and non-behavior-based features into a unified prediction framework. The model combines insights from both feature types, leveraging the strengths of each approach to achieve reliable predictions whether behavior data is available or not
Data Source
AI summary
The present disclosure is directed to generating and using a machine learning model, such as a neural network, by augmenting another machine learning model with an additional parameter. The additional parameter may be connected to some or all nodes of an internal layer of the neural network. A machine learning model can determine a value associated with the additional parameter using non-behavior or non-event-based information. The machine learning model can be trained using non-behavior or non-event-based information and parameter values of the other machine learning model.