Demographic Targeting via ML Feature Vectors
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Solution Overview
Problem
Conventional techniques for presenting electronic media content items to online audiences often result in poor user experience due to targeting a wide demographic, leading to wasted bandwidth and computing resources, as users are not always interested in the content, which reduces interactions and membership in social networks.
Innovation Solution
An online system employs a machine learning model to analyze past user interactions and content features to determine the most effective target audience for new content items, generating a demographic criteria vector to select users likely to interact with the content, thereby improving content relevance and reducing irrelevant content transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content items are targeted to a wide social demographic, then the reach of content items is increased, but the user experience deteriorates and user interactions decrease
Solution Approach 1:
The patent segments the wide social demographic into smaller, more homogeneous groups based on user profiles, interests, and interaction patterns. The system divides the broad audience into specific target demographics using clustering algorithms and machine learning models that analyze user behavior data, allowing content to be tailored to each segment's preferences while maintaining overall reach.
Solution Approach 2:
The system dynamically changes targeting parameters by adjusting demographic criteria, interest categories, and user profile attributes based on analyzed interaction patterns. The machine learning model continuously refines targeting parameters by processing user feedback and interaction data, optimizing which demographic segments receive which content items to improve user experience while maintaining reach.
2Quantity of substance
If content items are sent to users not interested in the content, then the reach is maintained, but networking bandwidth and computing resources are wasted
Solution Approach 1:
The system performs preliminary actions by pre-analyzing user profiles, interests, and interaction patterns before distributing content items. The machine learning model pre-computes user segmentation and content matching in advance, creating targeted distribution lists that identify which users are likely to interact with specific content. This preliminary targeting prevents wasteful transmission of content to uninterested users while maintaining appropriate reach.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with content items and using this data to refine future targeting decisions. The machine learning model processes interaction feedback (clicks, shares, ignores) to update user profiles and improve targeting accuracy, reducing resource waste on subsequent content distributions while preserving effective reach to interested users.
3Ease of operation
If conventional techniques target the same content item to a wide demographic, then distribution simplicity is maintained, but user interactions and membership growth are reduced
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically perform content targeting without manual intervention. The system autonomously analyzes user data, segments demographics, and determines optimal content distribution targets. This automated self-service approach maintains operational simplicity while dramatically improving user interactions and membership growth through precise targeting, eliminating the need for complex manual targeting processes.
Data Source
AI summary
An online system stores user profiles of users performing past user interactions with content items. The system receives a new content item and extracts a new feature vector from an image in the new content item using image analysis. The system generates, by a machine learning model, a demographic criteria vector based on the new feature vector. The machine learning model is configured based on the user profiles of the users performing the past user interactions with the plurality of content items to receive a feature vector for a content item and generate a demographic criteria vector based on the feature vector. The demographic criteria vector indicates a likelihood of a user with a user profile matching the demographic criteria vector interacting with the content item exceeding a threshold. The system sends the demographic criteria vector to a content provider for targeting the new content item.