ML Content Predictor for E-commerce Permutation Optimization
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Solution Overview
Problem
Existing e-commerce platforms face challenges in dynamically selecting contextually relevant content for display during online transactions, as they often lack real-time relevance and efficiency in content placement and duplication avoidance across webpages.
Innovation Solution
A programmed content predictor system utilizing machine learning models to select and prioritize content for display on webpages, based on customer data and available content items, by building a permutation quality table and determining the highest scoring content position-item combinations for real-time or near real-time display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to dynamically select content in real-time, then content relevance and effectiveness are improved, but system complexity and computational resources required increase
Solution Approach 1:
The content selection system is divided into separate functional modules: a quality score model that evaluates content items, a predictor that generates permutations of content positions, and a selector that chooses the optimal permutation. This segmentation allows each component to be optimized independently and reduces overall system complexity by modularizing the ML-based content selection process.
Solution Approach 2:
The system pre-calculates quality scores for content items and pre-defines page variants with multiple content position permutations before actual content selection is needed. This preliminary preparation reduces real-time computational requirements and enables faster content selection while maintaining high relevance through ML-based quality scoring.
2Productivity
If multiple content items are selected for display across webpages, then user engagement and revenue potential increase, but the risk of content duplication across pages increases
Solution Approach 1:
The system incorporates feedback mechanisms that track which content items have been selected and displayed across different webpages. This feedback information is fed back into the content selection process to prevent duplication, ensuring that high-engagement content is selected without repeating the same items across multiple pages during a single electronic transaction.
Solution Approach 2:
The content predictor serves multiple functions simultaneously: it selects optimal content items, determines their positions, prevents duplication across pages, and adapts to different webpage contexts. This multi-functionality is achieved through a unified ML-based model that considers all these factors in a single selection process, improving efficiency while preventing harmful content duplication.
3Reliability
If content positions are optimized for each webpage, then content effectiveness increases, but the time required for real-time selection increases
Solution Approach 1:
The system pre-calculates and stores multiple possible permutations of content positions for different page variants before actual content selection is needed. This preliminary generation of position arrangements, combined with pre-computed quality scores, enables rapid selection in real-time without requiring extensive computational time during the actual content choice moment.
Solution Approach 2:
The content selection system dynamically adapts to different webpage contexts and user states in real-time, adjusting content positions based on current transaction requirements. This dynamic capability is achieved through ML models that can quickly evaluate and select optimal permutations without requiring time-consuming recalculations, balancing effectiveness with speed.
Data Source
AI summary
Disclosed herein are a method and system that utilize a programmed content predictor to dynamically select electronic publishing content. In particular, the content predictor applies a selection model to select content for one or more selected webpages presented during an electronic transaction. The selection model utilizes a set of one or more machine learning models to select content based on calculated quality scores. The nature of the quality scores determined by the quality score model depend on the particular application. The predictor generates and populates a permutation quality table based on a set of selected content items and page variant, wherein the page variant defines locations of content positions within a webpage. The predictor then consumes the selection model to select a best permutation of content item-content position combinations to be returned for display on a webpage.