Dynamic User Group Sizing with Weight Thresholds for Media Relevance
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Solution Overview
Problem
Media item providers face challenges in selecting an appropriate group of users for a media item, often resulting in either an overly broad or insufficient user selection, leading to irrelevant content distribution and increased processing resources.
Innovation Solution
A system and method for dynamically adjusting the size of a user group by defining desired categories and weight thresholds, allowing media item providers to explicitly specify the initial and final group sizes, using machine learning to determine user probabilities and relevance scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the media item provider manually identifies users to include or exclude from the group, then the selection precision may be improved, but the device complexity and time consumption increase significantly
Solution Approach 1:
The system automatically performs user group selection by computing weights and probabilities based on user attributes and media item categories, eliminating the need for manual provider intervention. The platform self-adjusts the group composition by comparing user weights against dynamically calculated thresholds, achieving both precision and operational simplicity.
Solution Approach 2:
The patent replaces manual mechanical selection processes with automated computational systems. Machine learning models and probability calculations substitute human judgment, using algorithms to determine user-group correspondence based on weighted attributes rather than manual identification.
2Measurement precision
If the media item provider manually identifies users to include or exclude from the group, then the selection accuracy may be improved, but the time consumption increases
Solution Approach 1:
The system pre-computes user weights and probabilities before actual media item distribution. User profiles are pre-tagged with attribute weights, and the platform maintains ready-to-use probability scores that enable rapid group selection without real-time manual intervention, thus improving both accuracy and speed.
Solution Approach 2:
Manual user identification is replaced with automated probability-based selection systems. The platform uses pre-computed weights and threshold comparisons to rapidly determine group membership, eliminating time-consuming manual processes while maintaining high selection accuracy.
3Productivity
If the group size is increased to include more users, then the productivity increases, but the relevance of content distribution decreases
Solution Approach 1:
The system dynamically adjusts the weight threshold parameter based on the desired group size level. When the provider selects a larger group size, the threshold automatically decreases to include more users; when a smaller, more precise group is desired, the threshold increases. This parameter adjustment maintains the balance between reach and relevance.
Solution Approach 2:
The user group composition is made dynamic rather than static. The system continuously recalculates which users belong to the target group based on current weight thresholds and probabilities, allowing the group to adapt to different distribution needs while maintaining relevance through algorithmic selection rather than fixed criteria.
4Quantity of substance
If the weight threshold is lowered to include more users, then the group size increases, but the measurement precision of user-category correspondence decreases
Solution Approach 1:
The system accepts parameter changes in weight thresholds as intentional provider decisions. When the provider lowers the threshold to increase group size, the system transparently calculates and displays the expected precision level, allowing the provider to make informed decisions about the trade-off between quantity and precision.
Solution Approach 2:
The system provides feedback to the media item provider about the expected precision level when different weight thresholds are applied. This feedback mechanism allows providers to understand the impact of threshold adjustments on group composition quality, enabling them to select appropriate levels of precision versus quantity based on their distribution goals.
Data Source
AI summary
A media item to be provided to a group of users of a content sharing platform is identified. Each user is associated with one or more weights, indicating the probability of correspondence with a category associated with the media item. A request to dynamically change the group size from a first to a second level is received. A second weight threshold for the group of users corresponding to the second level is obtained. Upon receiving a content request from a client device associated with a user, it is determined whether the user's weight meets the second threshold. If so, the media item is provided to the client device.