Recommendation Engine
Collaborative filtering engine for personalized plan suggestions.
Overview
Built a recommendation system that suggests optimal health plans based on member profiles, behavior patterns, and similar user preferences.
Key Results
The Challenge
Members struggled to find the right health plans:
- Too many plan options causing decision paralysis
- Generic recommendations not personalized
- High plan switch rate due to poor matches
- No data-driven plan optimization
- Manual recommendation process
They needed intelligent, personalized recommendations.
The Solution
I built a hybrid recommendation system:
Collaborative Filtering
Matrix factorization to find similar member preferences.
Content-Based Filtering
Plan feature matching based on member profiles.
Behavior Clustering
K-means clustering to group similar members.
Real-Time Scoring
Redis-cached recommendations with fast retrieval.
Key Features
Personalized
Recommendations tailored to each member.
Hybrid Model
Combines collaborative and content-based filtering.
Real-Time
Instant recommendations with caching.
A/B Testing
Built-in experimentation framework.
Tech Stack
ML
Backend
Data
Infrastructure
Screenshots
Recommendations UI
Performance Analytics
Model Architecture
Achievements
- Increased plan conversion by 40%
- Reduced plan switch rate by 25%
- Sub-50ms recommendation latency
- 85% precision@10 in offline evaluation
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