ML-Based Resonated Connection Identification in Online Networks
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Solution Overview
Problem
Online connection network systems face challenges in promoting premium subscriptions effectively, particularly in identifying the most resonating information for a given member profile and discerning user intent, as members may engage for different purposes such as job searching or recruiting.
Innovation Solution
A machine learning-based method involving a member intent model and a relevance model is used to capture user intent and generate scores for connected member profiles, selecting the most resonating connections to present as social proof for premium services, based on their likelihood of benefiting from these services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional social proof methods are used to promote premium subscriptions, then the promotion can be implemented simply, but the effectiveness is low because it does not account for diverse user intents
Solution Approach 1:
The system dynamically changes the parameters of social proof presentation based on detected user intent. Different user intents (job searching, recruiting, networking) trigger different social proof strategies, such as showing different connection types or different success metrics, thereby improving promotion effectiveness without requiring a completely new system architecture
Solution Approach 2:
The patent replaces traditional rule-based or manual social proof selection with machine learning models that automatically analyze user behavior patterns and intent. This substitution of mechanical decision-making with intelligent algorithms enables the system to handle diverse user intents effectively while maintaining operational simplicity
2Measurement precision
If generic social proof is presented to all users, then the implementation is simple, but the relevance to individual user needs is low
Solution Approach 1:
The system implements feedback loops where user interactions with the platform are continuously monitored and fed back into the machine learning models. This feedback mechanism enables the system to refine its understanding of user intent over time and adjust social proof presentations accordingly, improving relevance accuracy through iterative learning rather than complex manual analysis
Solution Approach 2:
The machine learning models automatically perform user behavior analysis and intent detection without requiring manual intervention. The system serves itself by using its own data infrastructure and computational resources to analyze user patterns, reducing the operational complexity while maintaining high measurement precision
3Productivity
If machine learning models are used to identify resonated connections, then the promotion effectiveness is improved, but the computational resources and processing time increase
Solution Approach 1:
The system applies machine learning models selectively rather than uniformly to all users. Social proof is presented only when the ML models detect specific user intents that indicate high conversion potential, avoiding unnecessary computational expenditure on users who are unlikely to convert. This partial application of complex analysis maintains high conversion rates while reducing overall energy consumption
Data Source
AI summary
The technical problem of identifying relevant social proof information with respect to a premium service for a given member profile in an online connection network system is addressed by, first, capturing the associated member's intent based on the member's activity on the web site provided by the online connection network system. The determined intent is used as input into a relevance machine learning model that is executed to identify the member's connection who is a subscriber to the premium service and who has been identified as the most convincing resonated connection of the member with respect to subscribing to the premium service.