Machine Learning Model Selection for Selling Time Prediction
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Solution Overview
Problem
Current methods lack the ability to accurately determine how long it takes to sell an item, leading to uncertainties in inventory management, potential material surplus or shortages, and missed sales opportunities for sellers.
Innovation Solution
A machine learning model is trained to predict the time it takes to sell an item by weighting data points such as time of year, seller ratings, number of photos, and price, using techniques like neural networks and XGBoost, allowing for continuous improvement and adaptation to changing selling patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained to predict selling times, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the prediction task into multiple machine learning models, each trained on different data points and weighting schemes. This allows the system to divide the complex prediction problem into manageable components that can be executed independently and combined for the final prediction.
Solution Approach 2:
The patent changes parameters by training models with different weightings of data points (e.g., time of year, seller ratings, number of photos, price). By adjusting these parameters and weightings, the system optimizes prediction accuracy while managing model complexity through selective feature importance.
2Measurement precision
If multiple data points are weighted and analyzed, then prediction accuracy is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-training multiple machine learning models on historical data before actual predictions are needed. This allows the models to have their parameters and weightings optimized in advance, so that when actual predictions are required, the system can quickly apply the pre-configured models without extensive real-time computation.
Solution Approach 2:
The system uses feedback from actual selling outcomes to continuously refine and retrain the machine learning models. This feedback loop allows the models to improve their accuracy over time based on real-world performance data, adjusting their weightings and parameters to better predict future selling times.
Data Source
AI summary
A method for training and selecting machine learning models is provided. Data points associated with an item during a first time period and having a selling time associated with the item are obtained. The data points are provided to first and second machine learning models. Both the first and second machine learning models are trained with the data points. The first machine learning model predicts a first selling time using the data points. The second machine learning model predicts a second selling time using the data points. The first and second machine learning models are updatable with additional data points associated with a second time period. Each of the first selling time and the second selling time are compared with the selling time associated with the item. Based on the comparison, one of the first or second machine learning models is selected to predict selling times of future items.