AI

AI-Powered Recommendation Engine

Build a recommendation system for personalized product/content suggestions using collaborative filtering and ML.

Our role: AI Engineer

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AI-Powered Recommendation Engine product screenshot, built by Devs & Logics

The challenge

Cold start problem, real-time recommendations, scalability, and accuracy improvement over time.

What Devs & Logics delivered

Solution 1: Vector embeddings for user and item similarity
Solution 2: Collaborative filtering using matrix factorization
Solution 3: Content-based filtering with feature extraction
Solution 4: Hybrid approach combining multiple algorithms
Solution 5: Real-time inference using optimized models
Solution 6: A/B testing framework for algorithm comparison
Solution 7: Next.js for recommendation display
Solution 8: Supabase for user interactions and feedback

Outcomes

Click-through rate increased by 45%
Conversion rate improved by 30%
Personalization accuracy of 89%
Revenue increased by $500K+ annually

Technologies

PythonScikit-learnTensorFlowNext.jsSupabaseRedisFastAPITypeScript

saas mvp development

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