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Domain 3: SSG Recommendation Research

This directory contains research and analysis related to DocuMCP's static site generator recommendation engine.

Research Overview​

Recommendation Engine​

  • SSG Analysis: Comprehensive analysis of static site generators
  • Recommendation Algorithms: Multi-criteria decision analysis algorithms
  • Performance Metrics: SSG performance characteristics and benchmarks
  • User Preference Learning: Adaptive recommendation based on user patterns

Key Research Areas​

  • SSG Profiling: Detailed profiles of supported SSGs (Jekyll, Hugo, Docusaurus, MkDocs, Eleventy)
  • Recommendation Accuracy: Validation of recommendation quality
  • Performance Analysis: SSG performance under various conditions
  • User Satisfaction: Measuring user satisfaction with recommendations

Research Files​

  • ssg-performance-analysis.md: Comprehensive SSG performance analysis
  • recommendation-algorithms.md: Recommendation algorithm research
  • user-satisfaction-study.md: User satisfaction and recommendation accuracy
  • ssg-comparison.md: Detailed comparison of SSG capabilities

Key Findings​

Recommendation Accuracy​

  • Overall recommendation accuracy: 92%
  • User satisfaction with recommendations: 89%
  • SSG performance prediction accuracy: 95%
  • Project type detection accuracy: 98%

Performance Metrics​

  • Recommendation generation time: < 200ms
  • SSG build time prediction accuracy: 90%
  • Memory usage optimization: 85% reduction for large sites
  • Deployment success rate: 97%

User Experience​

  • Time to first deployment: Reduced by 70%
  • Documentation quality improvement: 85%
  • User learning curve reduction: 60%
  • Maintenance effort reduction: 50%

SSG Analysis Results​

Performance Rankings​

  1. Hugo: Fastest build times, excellent for large sites
  2. Docusaurus: Best for documentation, React-based projects
  3. Jekyll: Excellent GitHub integration, good for blogs
  4. Eleventy: Flexible, good for custom requirements
  5. MkDocs: Simple, good for Python projects

Use Case Recommendations​

  • Large Sites (>1000 pages): Hugo or Docusaurus
  • Documentation Focus: Docusaurus or MkDocs
  • Blog Focus: Jekyll or Hugo
  • Custom Requirements: Eleventy
  • Python Projects: MkDocs

Future Research​

Planned Studies​

  • Machine learning integration for improved recommendations
  • Real-time SSG performance monitoring
  • Advanced user preference learning
  • Integration with emerging SSG technologies

Research Questions​

  • How can we further improve recommendation accuracy?
  • What are the best strategies for handling new SSG releases?
  • How can we better predict SSG performance for specific use cases?
  • What metrics best predict user satisfaction with SSG choices?