Common Factor Matrices for Real-Time Engagement Prediction
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Solution Overview
Problem
Existing messaging systems face significant challenges in efficiently processing large volumes of data to provide real-time content recommendations due to the high processing power required for analyzing interactions and commonalities among millions of accounts, making it costly and resource-intensive.
Innovation Solution
The system uses a model that predicts engagement behavior by generating matrices based on historical data, including common factor matrices shared across multiple engagement types, reducing the number of model parameters and enabling efficient training and real-time access, thus allowing for more efficient content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes large volumes of engagement data from millions of accounts to provide real-time content recommendations, then the accuracy of recommendations improves, but the processing power and costs increase significantly
Solution Approach 1:
The patent segments the engagement data processing by dividing it into engagement-type-specific matrices and common factor matrices. Each engagement type (e.g., likes, comments, shares) has its own matrix, while shared patterns across types are captured in common factor matrices. This segmentation allows the system to process data more efficiently by focusing on specific engagement types separately while still capturing overall patterns, thereby maintaining prediction accuracy while reducing computational complexity.
Solution Approach 2:
The patent implements universality through common factor matrices that are shared across multiple engagement types. These common factors capture universal patterns in user behavior that apply across different engagement types, allowing the system to reuse the same computational structures for multiple purposes. This multi-functionality reduces the total number of parameters that need to be trained and stored, decreasing processing power requirements while maintaining comprehensive analysis capability.
2Measurement precision
If the system stores and processes engagement data separately for each engagement type, then the precision of type-specific analysis improves, but the total number of model parameters and processing time increase
Solution Approach 1:
The patent segments the model into type-specific matrices (one for each engagement type) and common factor matrices. This segmentation allows precise analysis of each engagement type while sharing computational work through the common factors. The type-specific matrices capture unique patterns for each engagement type, ensuring precision, while the common factors reduce redundancy and processing time by capturing shared patterns across all types.
Solution Approach 2:
The patent merges the processing of multiple engagement types by introducing common factor matrices that are shared across all engagement types. Instead of completely independent processing for each type, the system combines them through common factors that capture universal behavioral patterns. This merging reduces the total computational burden and processing time while maintaining the ability to analyze each engagement type with appropriate precision.
3Measurement precision
If the system uses separate factor matrices for each engagement type, then the accuracy of predictions for each type improves, but the device complexity and memory requirements increase
Solution Approach 1:
The patent segments the model parameters into type-specific components and common components. Each engagement type has its own specific factors that capture unique patterns, ensuring accurate predictions for each type. Meanwhile, common factors are extracted that represent patterns shared across multiple engagement types, reducing the total number of parameters needed while maintaining comprehensive coverage of user behavior patterns.
Solution Approach 2:
The patent implements universality through common factor matrices that serve multiple engagement types simultaneously. These common factors are universal representations that can be applied across different engagement types, reducing the need for separate parameters for each type. This multi-functionality decreases device complexity and memory requirements while preserving the ability to make accurate predictions for each specific engagement type.
Data Source
AI summary
A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.