Find-Based Look-Alike Recommender for Machine-Learning Audience Expansion
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Solution Overview
Problem
Existing multimedia content sharing applications fail to effectively tailor content to individual user interests, resulting in low engagement rates due to non-personalized content delivery, which is inefficient and resource-intensive when relying on manual audience identification.
Innovation Solution
A computer-implemented method using machine learning models and similarity models to analyze individual characteristics data, generate engagement scores, and expand target audiences by identifying similar individuals, optimizing content delivery to enhance engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual audience identification and content filtering is used, then content can be delivered to targeted audiences, but the process is significantly costly in terms of time and resources
Solution Approach 1:
The patent replaces manual audience identification and content filtering processes with automated machine learning models and natural language processing systems. These systems automatically analyze user profiles, content characteristics, and engagement patterns to identify target audiences and filter relevant content, eliminating the need for manual research and filtering while maintaining high precision in audience identification.
Solution Approach 2:
The system enables self-service by allowing the platform to automatically perform audience identification and content matching without human intervention. The machine learning models continuously learn from user interactions and automatically update audience segments, while the natural language processing system autonomously analyzes content and matches it with appropriate audiences based on learned patterns.
2Quantity of substance
If broadcast content delivery is used, then content can be pushed to large user base, but engagement rate is low due to lack of personalization
Solution Approach 1:
The patent applies local quality by customizing content delivery for different user segments based on their specific characteristics, preferences, and behaviors. Instead of uniform broadcast delivery, the system analyzes individual user profiles and delivers personalized content recommendations to each user or user segment, making the content relevant and engaging for each recipient while maintaining scalability across the entire user base.
Solution Approach 2:
The system segments the large user base into distinct audience groups based on demographics, interests, behaviors, and engagement patterns using machine learning clustering algorithms. This segmentation allows the platform to deliver tailored content to each segment, improving engagement rates while still reaching a broad audience through multiple targeted delivery channels.
3Measurement precision
If extensive research and manual filtering is performed to identify target audience, then accurate targeting can be achieved, but the process is significantly costly in terms of resources
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing user data, content metadata, and engagement patterns into structured formats that machine learning models can efficiently process. User profiles are pre-segmented based on available data, and content is pre-tagged with relevant characteristics, enabling rapid and accurate audience identification without requiring extensive real-time research and manual filtering when content needs to be delivered.
Data Source
AI summary
An input dataset corresponding to an audience is augmented by joining a first set of individual IDs of the audience with a second set of individual IDs in a set of reference audiences stored within a cloud system. An engagement score is generated for each individual in the first set of individual IDs by processing the input dataset with augmented data using a trained machine learning model. Each ID in the first set of individual IDs is assigned to a tier category. A target audience (a subset of the first set of individual IDs belonging to a particular tier category) is identified. A similarity score is calculated for each individual belonging to the target audience with the second set of individual IDs in the set of reference audiences. An expanded audience is generated based on the similarity scores.