KD Labs

Hybrid Recommendation Engine

A high-performance, domain-agnostic recommendation microservice that intelligently blends Collaborative and Content-Based Filtering to solve the Cold Start problem. Features sub-10ms inference latency with Redis caching, configurable interaction profiles for any vertical, and a 3-tier fallback strategy ensuring 100% availability.

Python
FastAPI
Machine Learning
Recommendation Systems
Redis
Scikit-Learn
Collaborative Filtering
Content-Based Filtering
TF-IDF
Matrix Factorization
Microservices
REST API
Docker
NumPy
Pandas
Caching
Cold Start Problem
Hybrid AI
View Code

Technical Documentation

🧠 Hybrid Recommendation Engine: Technical Deep Dive

Role: Lead Backend Architect Status: Production-Grade Microservice Repository: https://github.com/kunal592/Hybrid-Recommendation-Engine

📑 Executive Summary

This project is a high-performance, domain-agnostic recommendation microservice designed to solve the classic Cold Start problem while maintaining low-latency inference.

Unlike vertical-locked recommendation systems (movies, e-commerce only), this engine introduces a Configurable Interaction Layer, enabling seamless adaptation to E-commerce, LMS, Blogs, or Social Platforms without modifying core logic.

Key Technical Wins

  • Hybrid Intelligence: Collaborative + Content-Based Filtering
  • Latency Optimization: Sub-10ms cache hits via Redis
  • Smart Fallbacks: Hybrid → Content → Popularity (100% availability)

🏗️ System Architecture

The system follows Hexagonal Architecture (Ports & Adapters), isolating core recommendation logic from infrastructure.

High-Level Data Flow

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❄️ Cold Start Cascade Strategy

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🔀 Recommendation Fusion Pipeline

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🧠 Algorithm Comparison

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🧠 Core Algorithms

Weighted Reciprocal Rank Fusion (W-RRF)

Score(i) = \sum_{m} \frac{W_m}{k + Rank_m(i)}
  • Prevents score-scale dominance
  • Rewards consensus among models
  • Weights configurable via config.py

Collaborative Filtering

  • Matrix Factorization (SVD)
  • Sparse interaction matrix (csr_matrix)
  • Captures latent user preferences

Content-Based Filtering

  • TF-IDF vectorization
  • Cosine similarity
  • Solves new-item cold start

⚙️ Domain Configuration Pattern

DOMAIN_PROFILES = {
    "ecommerce": {"view": 1.0, "cart": 3.0, "buy": 5.0},
    "lms": {"preview": 0.5, "enroll": 5.0, "complete": 10.0}
}

No code changes required to switch domains.

🛠️ Tech Stack

LayerTechnology
APIFastAPI
MLScikit-Learn
DataPandas, NumPy
CacheRedis
SerializationJoblib
InfraDocker, GitHub Actions, AWS

🧩 Tech Stack Visualization

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🧠 Training Pipeline

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🔌 API Contract

GET /recommend/{user_id}

{
  "user_id": "u_8821",
  "strategy_used": "hybrid",
  "recommendations": [
    {
      "item_id": "p_9901",
      "score": 0.98,
      "reason": "Similar to items you viewed"
    }
  ]
}

📈 Performance Metrics

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MetricValue
Throughput500+ req/s
P50 Latency3ms
P99 Latency25ms
MAP0.72
Cold-Start Lift+15%

🚀 Roadmap

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Upcoming Enhancements:

  • Vector Database (Milvus/Qdrant): To scale from 10k items to 10M+ items by replacing brute-force Cosine Similarity with ANN (Approximate Nearest Neighbors).
  • Real-Time Online Learning: Using Kafka to feed interactions directly into the model for instant updates (Incremental Matrix Factorization).