case study · at TrulyMadly
Scoring the matches at TrulyMadly
At TrulyMadly, Divyanshu Aryan builds scoring systems for the match recommendations: the systems that decide whose profile people see next. This work was done as an employee of TrulyMadly and belongs to TrulyMadly. The rest of this page is in Divyanshu’s own words.
I built the scoring behind your matches.
Everything on a dating app starts with whose profile you see next. At TrulyMadly I built several of the scoring systems that make that call, proving each idea as a small prototype before it went live. One, which I built end to end, predicts which new users will subscribe. Together they improved the matches and grew subscriptions.
7×
likelier than average to subscribe: the top 1% of new users my model picks
for engineers
- End-to-end model predicting new-user paid conversion: dataset, feature engineering, XGBoost (0.78 ROC-AUC, 7× top-1% lift), deployed into production recommendations.
- Multiple scoring services deployed in the recommendation engine, improving ranking quality and subscription growth; POCs to validate features before rollout.
- Event-driven enrichment pipeline turning profiles into vision-LLM attributes and persona embeddings; migrated vector search from MongoDB Atlas to Qdrant behind an embedding microservice.