Machine Learning Price Bands for Relative Expensiveness and User Affinity
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Solution Overview
Problem
Existing systems struggle to understand the subjective expensiveness of items and user affinities across different item categories, making it difficult to personalize recommendations and improve conversion rates on electronic platforms.
Innovation Solution
A machine learning architecture that includes a price band determination model and an affinity prediction model to assign items to price bands based on relative expensiveness and analyze user interactions, generating personalized price affinity predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If absolute price values are used to determine item expensiveness, then the system can process pricing data simply, but it fails to capture the subjective expensiveness relative to item categories
Solution Approach 1:
The system transforms the pricing analysis from using absolute price values to using relative price ratios. By calculating the ratio between an item's price and the average price of its category, the system captures subjective expensiveness while maintaining computational efficiency. This parameter transformation resolves the contradiction by improving measurement precision without significantly increasing system complexity.
Solution Approach 2:
The system introduces category average prices as an intermediary reference point. Instead of directly comparing absolute prices, the system uses the category average as a mediator to determine relative expensiveness. This intermediary approach enables the system to capture nuanced pricing perceptions while keeping the analysis framework manageable.
2Loss of information
If the system analyzes user interactions with items across different categories, then it can understand user price preferences, but it becomes difficult to configure the system to learn what constitutes expensive or inexpensive items
Solution Approach 1:
The system changes the analytical parameter from absolute price thresholds to relative price ratios. By using ratios of item price to category average price, the system can generalize user preferences across categories without requiring category-specific configuration. This parameter transformation enables the system to learn user price affinities while maintaining a unified, simple configuration approach.
Solution Approach 2:
The system creates a universal pricing analysis framework that works across all item categories using the same price ratio methodology. Instead of configuring separate rules for different categories, the system applies a universal approach of comparing item prices to their respective category averages, enabling the system to handle diverse categories with a single configuration scheme.
3Reliability
If the system provides personalized recommendations based on price affinity, then user satisfaction improves, but the system requires sophisticated machine learning architectures
Solution Approach 1:
The system transforms the recommendation problem by using price ratios as the key feature instead of absolute prices. This parameter transformation simplifies the learning task for the machine learning model, as users' price preferences can be captured through their interactions with items of varying price levels within their preferred ratio ranges, reducing the complexity of the architecture needed to achieve accurate recommendations.
Solution Approach 2:
The system pre-calculates price ratios for all items and stores them as features before the recommendation process. By performing this preprocessing action in advance, the system reduces the computational burden during real-time recommendation generation, enabling sophisticated personalization with a more manageable architecture that leverages pre-computed features.
Data Source
AI summary
A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: evaluating, using a price band determination model, degrees of expensiveness of items relative to each other in item type categories; generating, using the price band determination model, price bands associated with item type categories; assigning each of the items to a respective one of the price bands associated with a respective one of the item type categories; and presenting, to one or more end-user applications, at least one other item corresponding to at least one of the price bands associated with at least one of the item type categories. Other embodiments are disclosed.