# Configuration template for the recommendation engine plugin.

# Data source configuration
data_source:
  type: "csv"  # Type of data source: csv, database, api, etc.
  path: "data/user_item_interactions.csv" # Path to the data file or API endpoint
  # Database connection details (if applicable)
  database:
    host: "localhost" # REPLACE_ME: Database host
    port: 5432       # Database port
    name: "recommendations_db" # REPLACE_ME: Database name
    user: "recommendations_user" # REPLACE_ME: Database user
    password: "YOUR_DATABASE_PASSWORD" # REPLACE_ME: Database password

# Feature engineering configuration
feature_engineering:
  user_features:
    - "age"       # List of user features to use
    - "location"
    - "interests"
  item_features:
    - "category"    # List of item features to use
    - "price"
    - "description"
  interaction_features:
    - "rating"    # List of interaction features to use
    - "timestamp"
  feature_scaling: "standard" # Feature scaling method: standard, minmax, none

# Model configuration
model:
  type: "collaborative_filtering" # Type of recommendation model: collaborative_filtering, content_based, hybrid
  algorithm: "als" # Algorithm to use for the model: als, sgd, knn, etc.
  hyperparameters:
    n_factors: 50  # Number of latent factors in collaborative filtering
    reg_param: 0.01 # Regularization parameter
    epochs: 10      # Number of training epochs
  cold_start_strategy: "popularity" # Strategy for handling cold start problems: popularity, average, etc.

# Evaluation configuration
evaluation:
  metrics:
    - "precision"   # List of evaluation metrics to use
    - "recall"
    - "ndcg"
  split_ratio: 0.8  # Ratio of data to use for training (remaining for testing)
  k: 10           # Number of recommendations to consider for evaluation

# Deployment configuration
deployment:
  model_path: "models/recommendation_model.pkl" # Path to save the trained model
  api_endpoint: "/recommendations" # API endpoint for serving recommendations
  num_recommendations: 5  # Default number of recommendations to return

# Logging configuration
logging:
  level: "INFO"   # Logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL
  file: "logs/recommendation_engine.log" # Path to the log file

# Performance optimization configuration
optimization:
  enable_caching: true # Enable caching of recommendations
  cache_expiry: 3600  # Cache expiry time in seconds (1 hour)

# Error handling configuration
error_handling:
  retries: 3       # Number of retries for failed requests
  retry_delay: 60   # Delay between retries in seconds