ML Classification for Landing Page Feature Transformation
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Solution Overview
Problem
Current digital content processing technologies face challenges in improving the quality of online advertisements, as measured by dwell time, which affects user engagement and long-term revenue, with existing methods prioritizing short-term revenue over long-term gains.
Innovation Solution
Implementing machine learning (ML) classification techniques to adjust features of digital content, such as advertisements, by determining a post-click experience threshold and using feature importance to transform lower-quality landing pages into higher-quality ones, thereby enhancing user experience and revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If machine learning classification is used to transform lower-quality landing pages into higher-quality ones, then dwell time and user engagement are improved, but the complexity of the system increases
Solution Approach 1:
The patent applies parameter changes by adjusting feature values of digital content (such as modifying characteristics of advertisements or landing pages) to transform lower-quality content into higher-quality content. This involves changing parameters like content relevance, quality metrics, or engagement characteristics to achieve the desired transformation and improve dwell time.
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates the quality of digital content based on metrics like dwell time and user engagement, then uses this feedback to automatically adjust and transform content features. This closed-loop approach enables continuous improvement of content quality without requiring manual intervention.
2Productivity
If digital content features are adjusted to improve quality, then user engagement increases, but the difficulty of detecting and measuring quality improvements increases
Solution Approach 1:
The patent replaces manual quality assessment methods with automated machine learning classification systems. Instead of relying on subjective human evaluation or complex manual measurement processes, the system uses computational models to automatically detect, measure, and evaluate content quality based on defined features and metrics, thereby simplifying the measurement process.
Solution Approach 2:
The patent introduces intermediate metrics and features that serve as proxies for quality assessment. Rather than directly measuring complex quality attributes, the system uses measurable intermediate parameters (such as dwell time, click-through rate, or feature-based scores) that correlate with quality and facilitate automated detection and measurement.
Data Source
AI summary
Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for changing a classification of a landing page, such as via, for example, identifying features of the landing page, such as to predict a binary classification of the landing page as to post-click user experience. One or more adjustments to features of the landing page may be determined, such as using a machine learning approach, by way of non-limiting example.