Online System Target Audience Refinement via Interaction Analysis
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Solution Overview
Problem
Content providers face challenges in determining the most effective target audience for their content, often resulting in wasted resources as broad criteria lead to content being sent to users who are unlikely to interact with it, causing inefficiencies in online content distribution.
Innovation Solution
An online system that analyzes user profiles and content attributes to refine target audience criteria, identifying subsets of users with a higher likelihood of interest by matching user attributes with content attributes, thereby optimizing content distribution and reducing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content providers use broad target audience criteria to maximize content distribution reach, then the quantity of users receiving content increases, but resource wastage increases as content is sent to users unlikely to interact with it
Solution Approach 1:
The patent segments the broad target audience into distinct user subsets based on their interaction histories and attributes. By dividing the audience into segments with different likelihoods of interaction, the system can selectively distribute content to high-value segments while excluding low-value segments, thereby reducing resource wastage while maintaining effective reach.
Solution Approach 2:
The system dynamically changes the targeting parameters from broad demographic criteria to refined criteria based on observed user interaction patterns. By adjusting the selection parameters to include interaction history metrics and predicted likelihood scores, the system optimizes content distribution to reach users most likely to engage, reducing waste while preserving reach.
2Loss of energy
If content providers specify narrow target audience criteria to reduce resource wastage, then resource efficiency improves, but the reach and potential audience size decreases
Solution Approach 1:
The patent implements dynamic target audience selection that adapts based on real-time interaction data. Rather than using static narrow criteria, the system continuously updates user profiles and interaction likelihood predictions, allowing the target audience definition to expand or contract dynamically. This maintains resource efficiency while ensuring optimal reach to users who are currently most likely to interact.
Solution Approach 2:
The system incorporates feedback loops where user interactions are continuously monitored and fed back into the targeting model. This feedback mechanism allows the system to learn from actual user behavior and refine target audience selection, ensuring that resource efficiency is maintained while reach is optimized to the most responsive users.
3Ease of operation
If online systems send content to all users regardless of interest level, then content distribution simplicity is maintained, but interaction rates decrease due to sending content to uninterested users
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing user interaction likelihood scores and profile attributes before content distribution occurs. This pre-processing creates ready-to-use targeting criteria that simplify the actual distribution process, maintaining operational ease while significantly improving interaction rates through pre-filtered, high-probability target selection.
Solution Approach 2:
The system introduces an intermediary layer of interaction likelihood prediction and profile matching between the content and users. This intermediary mechanism automatically evaluates and ranks potential recipients based on multiple attributes, bridging the gap between simple distribution and targeted delivery, thereby maintaining simplicity while enhancing interaction reliability.
Data Source
AI summary
An online system receives a content item from content providers and a target audience criterion and for targeting the received content item. The online system determines recommendations of modifications of the target audience criteria such that users of the modified target audience criteria have a higher likelihood of interacting with the content item compared to the received target audience criteria. The online system stores measures of interest of subsets of users for different topics. The online system determines topics associated with the content item and determines measures of interest of subsets of users in the content item based on topics associated with the content item. The online system provides recommendations for modifying the target audience criteria based on the determined measures of interests of users.