User Influence Scoring for Targeted Message Distribution
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Solution Overview
Problem
Existing methods for delivering messages to specific subsets of users within a larger community are inefficient, as they require defining relevance criteria and evaluating each recipient individually, leading to increased communication complexity and time, especially when the message is of limited relevance.
Innovation Solution
A system and method for identifying influencers within a user pool based on past interactions, using popularity and earliness of interaction metrics to calculate a prescience metric, which helps in targeting relevant users by analyzing associative artifacts and communication history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If messages are delivered to the broader audience using traditional mechanisms, then message delivery efficiency is improved, but message relevance to individual recipients deteriorates
Solution Approach 1:
The patent segments the user community into distinct clusters based on item affinity profiles. Instead of treating the audience as a monolithic group, the system divides users into segments with similar preferences and behaviors, enabling targeted message delivery to specific segments while maintaining overall delivery efficiency.
Solution Approach 2:
The system performs preliminary analysis of user item affinities and communication histories before message delivery. By pre-computing user profiles, clustering users into segments, and predicting affinities in advance, the system prepares the groundwork for efficient targeted delivery without requiring real-time evaluation of each recipient.
2Loss of information
If criteria are defined and individual evaluation is performed to discover relevant recipients, then message relevance is improved, but communication complexity and time increase
Solution Approach 1:
The patent creates simplified copies of user profiles and affinity patterns that can be quickly matched against message criteria. Instead of performing complex individual evaluations, the system uses pre-computed user representations that capture essential preferences, enabling rapid relevance determination without full individual analysis.
Solution Approach 2:
The system transforms complex user evaluation criteria into simplified parameters and metrics. By changing the representation of user characteristics into standardized affinity scores and cluster memberships, the system reduces communication complexity while maintaining the ability to assess message relevance accurately.
3Loss of information
If criteria are defined and individual evaluation is performed to discover relevant recipients, then message relevance is improved, but communication time increases
Solution Approach 1:
The system performs all necessary user analysis, clustering, and affinity computation before message delivery. By completing these evaluations in advance, the system eliminates time-consuming individual assessments during actual message delivery, achieving both high relevance and fast communication.
Solution Approach 2:
The system maintains continuous updates of user profiles and affinity measurements as new data becomes available. This continuous preparation ensures that when messages need to be delivered, the evaluation work has already been done or is readily available, minimizing communication time while maintaining relevance accuracy.
Data Source
AI summary
Systems and methods are provided for granting a permission. A similarity score is calculated for each of a plurality of items and a particular item. A score is determined for each person in a pool for each of the plurality of items, wherein prescience scores are calculated based on an earliness of interaction metric and a number of interactions metric. For each person, a permission score is calculated for the particular item based on similarity scores for items and that person's score for items. A permission is granted to one or more people based on said permission scores.