spotify-swipe-based-recommendation-system

Phase 1: Research and EDA

• Objectives:
  • Gather data on user preferences and behavior patterns in music applications.
  • Define project requirements and technical specifications + EDA.
• Deliverables:
  • Project plan document.
  • Technical requirements and architecture.
  • Initial data collection and analysis report.

Phase 2: Development of Song Popularity Prediction Model

• Objectives:
  • Develop a machine learning model to predict song popularity based on Spotify’s data such as play counts, user interaction, and metadata.
  • Validate the model with historical data and adjust based on performance.
• Deliverables:
  • Machine learning model trained and tested.
  • Documentation on model performance and metrics.

Phase 3: Implementation of the Recommendation System

• Objectives:
  • Build the backend for the recommendation system integrating the song popularity model.
  • Implement algorithms for mood-based matchmaking, genre roulette, etc.
  • Start with basic swipe interactions and feedback mechanisms.
• Deliverables:
  • Recommendation system backend.
  • Initial version of the swipe interface for internal testing.

Phase 4: App or Web Extension Development

• Objectives:
  • Decide whether to develop a standalone app or a web extension based on ease of integration with Spotify and user accessibility.
  • Develop a swipe-based interface for the app or web extension.
  • Incorporate user feedback mechanisms into the interface.
• Deliverables:
  • Beta version of the app or web extension.