AI Matchmaking Algorithms: Performance Analysis and Compatibility Prediction
Study Period
Jan – Dec 2024
12-month comprehensive analysis
Sample Size
n=3,200
Diverse participant pool
Study Design
6-month + A/B
Longitudinal tracking
Protocol
v2.1
DHR-ETH-2024 framework
Abstract
This comprehensive AI matchmaking study analyzes the performance and predictive accuracy of AI-driven matchmaking algorithms across three major dating platforms. Through controlled A/B testing and longitudinal tracking of 3,200 users over 6 months, we evaluate how machine learning models compare to traditional matching methods in predicting long-term compatibility and user satisfaction.
Key Statistical Findings
42%
higher 6-month retention
for algorithms prioritizing long-term compatibility vs basic matching
28%
higher user satisfaction scores
reported for AI-driven matches compared to traditional methods
Research Questions
How accurately do AI algorithms predict long-term relationship success compared to human intuition?
Which compatibility factors are most accurately weighted by machine learning models?
How does algorithm transparency affect user trust and engagement?
Methodology Highlights
- Controlled A/B testing across three platforms
- Six-month longitudinal participant tracking
- Mixed-methods analysis combining quantitative and qualitative data
- Ethical review board approval (DHR-ETH-2024)