Social User Matching Using Dynamic Relevancy Scoring
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Solution Overview
Problem
Existing social networking systems struggle to efficiently identify and introduce relevant users to each other in online environments, failing to consider dynamic user interactions and preferences, leading to suboptimal user connections and experiences.
Innovation Solution
A system and method that utilizes a host server to analyze user interactions, generate representations of social connections, and calculate relevancy scores based on frequency and responsiveness, selecting and presenting relevant users to enhance user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system analyzes user interactions and calculates relevancy scores to select relevant users for introduction, then user experience and connection quality are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the user base into different groups based on interaction frequency and relevancy scores, allowing targeted introductions rather than analyzing all possible user pairs. This divides the complex problem of user matching into manageable segments that can be processed efficiently.
Solution Approach 2:
The system changes parameters by calculating dynamic relevancy scores based on interaction frequency and user preferences, then uses these scores to prioritize which user introductions to make. This parameter-based approach transforms the matching problem into a scoring and ranking task that is more computationally tractable.
2Measurement precision
If the system tracks and analyzes dynamic user interactions in real-time, then user preference accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing interaction frequency metrics and user preference profiles before they are needed for matching. This allows the system to have user data ready in advance, reducing processing time when introductions need to be made while maintaining accurate preference measurement.
3Productivity
If the system introduces more relevant users to each user, then user engagement is improved, but information overload and user fatigue increase
Solution Approach 1:
The system applies local quality by tailoring the number and type of user introductions to each individual user's preferences and engagement patterns. Rather than introducing the same number of users to everyone, the system adjusts introduction quantity and quality locally for each user based on their specific characteristics and responsiveness.
Data Source
AI summary
Systems and methods of selecting a relevant user for introduction to a user in an online environment are disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of receiving a request to identify the relevant user to be introduced to the participating user in the social network, identifying a set of social history records that occurred among the multiple users in the social network, selecting, from the multiple users, the relevant user to be introduced to the participating user using the set of social history records. The relevant user that is selected is not a social connection of the participating user in the social network. One embodiment includes, determining whether the participating user is interested in meeting the relevant user in the social network based on an indication made by the participating user via interaction with the user device.