Content Distribution Model Using Proxy Metrics for User Retention
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Solution Overview
Problem
Existing content distribution systems struggle to optimize content presentation based on user interest and engagement metrics, especially when direct feedback is lacking, leading to suboptimal user experience and inefficient content selection.
Innovation Solution
A system utilizing a training database, content database, and model generator to analyze proxy metrics such as clicks, watch time, and viewability to predict user engagement, enabling the selection of content that maximizes subject retention or awareness, even in the absence of direct user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional content distribution systems are used, then content can be presented to users, but the system cannot optimize content selection based on user interest and engagement metrics
Solution Approach 1:
The patent introduces a model as an intermediary between the content distribution system and user data. This model predicts user engagement metrics by processing available user attributes and content features, enabling optimized content selection without direct access to complete user engagement data. The model acts as a mediator that translates limited input data into actionable engagement predictions.
2Productivity
If direct user feedback data is collected, then content optimization can be improved, but data storage requirements increase
Solution Approach 1:
The patent extracts only the essential user attributes and content features needed for prediction, storing them in compact formats. Instead of storing complete user feedback datasets, the system extracts key predictive features and stores condensed representations that can be processed by the model to generate engagement predictions, significantly reducing storage requirements while maintaining optimization accuracy.
3Adaptability or versatility
If user data is stored for analysis, then content selection can be personalized, but system complexity increases
Solution Approach 1:
The patent creates a simplified computational model that copies only the essential patterns of user behavior needed for content selection. Instead of implementing complex data storage and processing infrastructure, the system uses a model that replicates key user engagement patterns through algorithmic processing of available features, achieving personalization with reduced system complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing content presentation. In one aspect, a system includes a training database that stores training data including attribute information about users and corresponding proxy metrics quantifying behavior by the users following content presentation; a content database; a model generator that accesses the training data and trains a model for content distribution; and a content distribution server that receives a content request, uses the model to select content, transmits data identifying the selected content, wherein the model: obtains a set of attributes for a user associated with the request, receives information about a given content, predicts a proxy metric based on the set of attributes and the information about the content, the predicted proxy metric providing information about subject retention or awareness; and identifies the given content for distribution if the predicted proxy metrics meet a threshold.