Recommendation Prediction Calibration for High-Rank Bias Correction
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Solution Overview
Problem
Existing recommendation systems suffer from prediction bias, particularly overestimating items with high rank prediction values, leading to user disappointment and inventory issues due to inaccurate stock management.
Innovation Solution
An information processing apparatus and method that includes a predictor for evaluating user preferences, a high rank extraction unit, and a corrector to train and adjust prediction values based on verification data, ensuring accuracy by reducing bias through calibration models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a recommendation system uses prediction values to recommend items with high predicted evaluation values, then user recommendations are provided efficiently, but prediction bias occurs causing overestimation of high rank items
Solution Approach 1:
The patent segments the prediction correction process by rank level. It divides items into different rank categories (high rank, medium rank, low rank) and applies different correction strengths to each segment. High rank items receive stronger correction to reduce overestimation bias, while other items use standard correction. This segmented approach resolves the contradiction by maintaining recommendation efficiency for all items while specifically improving prediction accuracy for high rank items that suffer from overestimation bias.
Solution Approach 2:
The patent changes the correction parameter dynamically based on the prediction rank. The correction amount is adjusted as a parameter according to the item's rank position, with higher correction applied to high rank items and standard correction to others. This parameter change strategy allows the system to maintain overall recommendation efficiency while accurately correcting the prediction bias that specifically affects high rank items, thereby resolving the measurement precision issue.
2Productivity
If prediction values are used for stock management decisions, then inventory turnover is improved, but overestimation leads to excess stock and increased holding costs
Solution Approach 1:
The patent applies segmentation to stock management by differentiating correction strategies based on item rank. High rank items that drive inventory turnover decisions receive enhanced correction to eliminate overestimation bias, preventing excess stock accumulation. Meanwhile, standard correction is applied to other items, maintaining adequate inventory levels without unnecessary holding costs. This resolves the contradiction between improving inventory turnover and preventing excess stock.
Solution Approach 2:
The patent changes the correction parameter according to item rank to optimize stock management. By adjusting the correction amount as a variable parameter for high rank items specifically, the system achieves more accurate demand prediction for items that most impact inventory turnover, while avoiding over-correction for other items. This parameter adaptation prevents both excess stock and stockouts, resolving the contradiction between inventory turnover improvement and excess stock prevention.
3Measurement precision
If standard correction is applied to all prediction values, then overall accuracy improves, but bias in high rank items persists
Solution Approach 1:
The patent segments the correction application by rank level, applying standard correction to most items while applying enhanced correction specifically to high rank items. This segmentation ensures that overall prediction accuracy is maintained through standard correction, while high rank prediction reliability is specifically improved through targeted enhanced correction. The segmentation approach resolves the contradiction by addressing the reliability issue for high rank items without compromising overall accuracy.
Solution Approach 2:
The patent applies local quality by making the correction strength location-dependent on the item's rank position. High rank items receive a different (stronger) correction quality compared to other items. This local differentiation ensures that the specific reliability problem affecting high rank predictions is addressed with appropriate correction intensity, while maintaining standard correction quality for other items, thereby resolving the contradiction between overall accuracy and high rank reliability.
Data Source
AI summary
There are provided an information processing apparatus, an information processing method, and a program that reduce a bias by accurately correcting a prediction value of an item having a high rank prediction value. A processor is configured to: acquire verification data for verifying a relationship between a prediction value output from a predictor that predicts an evaluation of a user for a candidate item and a true value of the evaluation of the user for the candidate item; input the verification data to the predictor, and acquire a high rank item which is a candidate item of which a rank of the output prediction value is relatively high; extract high rank data corresponding to the high rank item from the verification data; and train a corrector that corrects an input prediction value such that the input prediction value is close to the true value based on the high rank data.