ML-Based User Matching for Social Networks
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Solution Overview
Problem
Existing social network search methods are tedious and unreliable for finding suitable users, such as mentors, due to the large volume of unstructured and structured data, requiring manual keyword entry and profile review, which is time-consuming and inefficient.
Innovation Solution
A machine learning model is trained to cluster users based on feature profiles extracted from social network data, including unstructured text and group participation, allowing for efficient computation of similarity scores between users, presenting the initiating user with a short list of best matches for communication session establishment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual keyword entry and profile review methods are used to find suitable users, then users can search for mentors, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical search operations with an automated machine learning system. The ML model automatically processes user profiles, extracts features, computes similarity scores, and ranks potential matches, eliminating the need for manual keyword entry and profile review while dramatically improving matching efficiency and reducing time consumption.
Solution Approach 2:
The system enables self-service matching where users simply provide basic search criteria and the ML model autonomously performs the entire matching process. The system automatically clusters users, computes similarity scores based on extracted features, and presents ranked results without requiring manual intervention, making the process both efficient and user-friendly.
2Measurement precision
If comprehensive user profile data is analyzed to improve matching accuracy, then better user matches can be found, but processor and network utilization increases
Solution Approach 1:
The patent extracts only the most relevant features from comprehensive user profiles using the ML model. Instead of processing entire profiles, the system identifies and extracts key features that contribute most to matching accuracy, such as skills, experience levels, and compatibility metrics. This selective extraction maintains high matching precision while dramatically reducing computational overhead and resource utilization.
Solution Approach 2:
The matching process is segmented into distinct stages: feature extraction, similarity score computation, and result ranking. The ML model processes user data in modular fashion, computing similarity scores for different feature sets separately and combining results. This segmentation allows for optimized resource usage at each stage while maintaining overall matching accuracy.
Data Source
AI summary
There is provided a method, comprising: extracting user feature profiles for users of a social network, each feature profile being structured and including user features extracted from unstructured user generated text, indications of participation in groups, and structured user profiles, training a clustering-component of a model to cluster the feature profiles, training a matching-component of the model to compute a distance score indicative of statistical similarity between a feature profile of a target user and features profiles of other users of a same cluster, using a training dataset of pairs of feature profiles extracted from common clusters, each pair assigned a distance score label, providing the model for: identifying a certain cluster of a certain user, and computing distance scores between the feature profile of the certain user and other feature profiles of other users of the certain cluster for selecting one user for establishment of a communication session.