Machine-Learned Price Bands for Category-Based Affinity Prediction
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Solution Overview
Problem
Existing systems struggle to understand the subjective expensiveness of items and user affinities across different categories, making it difficult for electronic platforms to enhance customer satisfaction and conversion rates.
Innovation Solution
A machine learning architecture is employed to determine price bands and user price affinities by evaluating item prices, generating price bands with defined ranges, and analyzing user interactions to predict preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If absolute price values are used to determine item expensiveness, then the system is simple to implement, but it fails to capture subjective expensiveness relative to item categories
Solution Approach 1:
The system transforms the single parameter of absolute price into multiple parameters including relative price positioning within categories, price band assignments, and category-specific price contexts. This allows the system to capture subjective expensiveness by comparing prices against category norms and historical data rather than treating all prices equally.
Solution Approach 2:
The system segments the marketplace into distinct item categories (e.g., electronics, clothing, groceries) and further segments prices within each category into meaningful bands or tiers. This segmentation enables category-relative price assessment, recognizing that a $100 item means different things in different categories.
2Adaptability or versatility
If the system attempts to understand subjective expensiveness across all item categories, then customer satisfaction improves, but the complexity of configuring and maintaining the system increases
Solution Approach 1:
The system automatically learns and adapts to category-specific price patterns through machine learning algorithms that analyze historical sales data, user behavior, and market conditions. Rather than requiring manual configuration for each category, the system self-adjusts to understand what constitutes expensive, medium-priced, and inexpensive items within each category context.
Solution Approach 2:
The system implements a universal framework that handles multiple item categories simultaneously through a single cohesive architecture. The same underlying mechanisms (price band determination, affinity prediction, recommendation engines) apply across all categories, reducing configuration complexity while maintaining category-specific accuracy.
3Measurement precision
If the system collects and analyzes detailed user interaction data to predict price affinity, then prediction accuracy improves, but data processing requirements and computational resources increase
Solution Approach 1:
The system extracts and focuses on the most predictive features from user interaction data, such as click patterns, purchase history, time spent on product pages, and cart abandonment behavior. By identifying and extracting only the most relevant signals rather than processing all available data equally, the system maintains high prediction accuracy while reducing computational overhead.
Solution Approach 2:
The system implements a two-stage approach where it first makes rapid preliminary assessments using lightweight models, then applies more computationally intensive analysis only when needed (e.g., for high-value items or uncertain predictions). This partial application of full analysis resources optimizes the balance between accuracy and computational cost.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instruction that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a training procedure, labels based at least in part on price band activity data from a time period; training, using the training procedure, an affinity prediction model of a machine learning architecture; analyzing, using the affinity prediction model of the machine learning architecture, as trained, the price band activity data indicating interactions of a user with items; and generating, using the labels and the affinity prediction model of the machine learning architecture, as trained, one or more price affinity predictions for one or more items for the user. Other embodiments are disclosed herein.