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Embedding-Based Recommendation Engine Development for Multi-Content Subscription Platform
  1. case
  2. Embedding-Based Recommendation Engine Development for Multi-Content Subscription Platform

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Embedding-Based Recommendation Engine Development for Multi-Content Subscription Platform

kitrum.com
Media
eCommerce
Information technology

Current Recommendation Limitations

Existing collaborative filtering model fails to deliver personalized cross-content recommendations, struggles with semantic similarity detection between diverse user preferences, and cannot scale effectively with growing content variety. Platform lacks ability to identify hidden user behavior patterns across different content types.

About the Client

Digital subscription platform offering books, audiobooks, news, and multimedia content across mobile and web platforms

Key Project Goals

  • Implement embedding-based retrieval system for semantic content analysis
  • Enhance personalization across multiple content formats
  • Improve cross-domain recommendation accuracy
  • Achieve scalable content comparison capabilities

Core System Requirements

  • Vector embedding generation for all content types
  • Semantic similarity detection across books/magazines/audio
  • Real-time user behavior tracking and analysis
  • Cross-content recommendation engine
  • Dynamic user profile updating

Technology Stack

Deep learning frameworks (TensorFlow/PyTorch)
Vector embedding models
Apache Spark for data processing
Cloud-based ML platforms
Containerization (Docker/Kubernetes)

System Integrations

  • Existing user content database
  • Cross-platform analytics tracking
  • Cloud storage for vector databases
  • Current content delivery infrastructure

Operational Requirements

  • Support 100M+ content items in vector database
  • Recommendation latency <200ms
  • 99.9% system availability
  • Horizontal scalability for user growth
  • Data privacy compliance (GDPR/CCPA)

Expected Business Outcomes

Anticipated 30% increase in recommendation-driven content consumption, 25% higher user retention through improved personalization, and 40% faster content discovery. System should enable cross-format recommendations that increase average session duration by 20% while maintaining 99.95% service reliability during peak usage.

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