Lidify

Lidify is an intelligent music discovery tool specifically designed to enhance Lidarr-managed music collections. By leveraging Spotify and LastFM APIs, it analyzes existing artist preferences and provides targeted recommendations to help users discover new music that aligns with their tastes and expands their collections intelligently.

Key Features

• Intelligent Music Discovery:

  • Artist-based recommendation algorithms
  • Album suggestion and discovery features
  • Similar artist identification and matching
  • Genre exploration and cross-pollination
  • Collection gap analysis and completion suggestions
  • Preference learning and adaptation

• Comprehensive API Integration:

  • Spotify API integration for rich music data
  • LastFM API support for scrobbling and recommendations
  • Multiple music data source utilization
  • Recommendation engine API access
  • Metadata retrieval and enhancement
  • Cross-platform music service integration

• Seamless Lidarr Integration:

  • Direct integration with existing Lidarr installations
  • Artist selection and preference analysis
  • Library synchronization and updates
  • Automatic artist addition and management
  • Collection preference learning and optimization
  • Workflow integration and automation

• Advanced Recommendation Engine:

  • Sophisticated similarity matching algorithms
  • Preference-based recommendation scoring
  • Discovery logic optimization and refinement
  • Quality filtering and curation
  • Relevance scoring and ranking
  • Personalized suggestion generation

• Comprehensive Discovery Management:

  • Recommendation history tracking and analysis
  • Discovery success rate monitoring
  • Collection expansion statistics and insights
  • Artist management and organization
  • Collection growth analytics and reporting
  • Preference trend analysis and visualization

• Flexible Configuration and Customization:

  • API authentication and connection setup
  • Discovery preference configuration and tuning
  • Recommendation filtering and criteria setting
  • Collection rule definition and management
  • Quality threshold configuration and optimization
  • Custom discovery workflow setup

• Self-Hosting Benefits:

  • Complete control over music discovery and recommendations
  • Privacy protection for music preferences and listening habits
  • Custom configuration for specific musical tastes
  • Integration with existing music automation infrastructure
  • No dependency on external recommendation services
  • Enhanced music discovery and collection building

Technical Specifications

• License: MIT
• Deployment: Docker containers
• Platforms: Cross-platform via Docker
• Requirements: Docker, API keys (Spotify/LastFM)
• Backend: Python
• Architecture: Recommendation service with API integration

Use Cases

• Collection Expansion: Systematically growing music libraries with relevant content
• Music Discovery: Finding new artists and albums based on existing preferences
• Genre Exploration: Discovering music across different genres and styles
• Recommendation Automation: Automated suggestion generation for Lidarr libraries
• Preference Analysis: Understanding musical tastes and collection patterns
• Curated Discovery: Quality-focused music recommendation and curation

Unique Advantages

• Lidarr-Focused: Specifically designed for Lidarr integration and workflow
• Multi-Source: Support for both Spotify and LastFM recommendation sources
• Discovery-Oriented: Specialized for music discovery and collection expansion
• Preference-Based: Intelligent learning from existing collection preferences
• Automation-Ready: Seamless integration with existing music automation
• Open Source: MIT licensed, transparent and customizable

⚠️ Note: This application requires API keys from Spotify or LastFM and may be subject to their service limitations and terms.

Based on the Lidify GitHub repository, this tool provides users with an intelligent music discovery solution that excels in preference-based recommendations and collection expansion, making it ideal for music enthusiasts and collectors who use Lidarr and want to systematically discover new music that aligns with their existing tastes.